Directory of all Master Modules
Module Number Module Title ECTS Lecture Type
Last offered Planned for Frequency Language of instruction
Assigned Study Areas Lecturer

Module Number

INFO-4311
Module Title

Modeling and Analysis of Embedded Systems
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction German
Type of Exam

Oral examination (written exam if there are a large number of participants), exercise points can included as a grade bonus in the assessment of the exam

Content

Embedded systems are a fundamental component in many technical systems and have become an integral part of everyday life, e.g. in mobile communications, medical technology, consumer electronics, the smart home, (fully) automated vehicles, industrial automation, and the Internet-of-Things (IoT). The associated extensive requirements for embedded systems with the manifold dependencies between software and hardware require application-specific design methods for embedded software. This module introduces modeling, analysis, and implementation techniques that consider the interaction of software with the underlying hardware architecture in terms of performance, energy efficiency, reliability, and functional safety at an early stage. Current research and development trends in embedded systems design are highlighted to introduce students to a topic of high industrial relevance at an early stage, providing both basic theoretical knowledge and domain-specific application skills.

Objectives

This module enables students to develop embedded systems and to compare specification techniques for embedded systems with each other and they are confronted with problems from the field of embedded systems that are relevant for science and industry. The students know the theoretical approaches to modelling and analysing embedded software, taking into account task scheduling, priority inversion, communication overhead as well as the influences of the hardware architecture, and can apply these to different practical problems in the design of embedded software systems. The exercises are worked on independently by the students in small groups and self-confidence, rhetorical skills and critical faculties are trained by demonstrating the achieved results.

Prerequisite for participation There are no specific prerequisites.
Lecturer Bringmann
Literature / Other

• O. Bringmann, W. Lange, M. Bogdan: Eingebettete Systeme: Entwurf, Modellierung und Synthese; De Gruyter Oldenbourg, 3. überarbeitete Auflage, 2018.
• P. Marwedel: Embedded System Design – Embedded Systems Foundations of Cyber-Physical Systems, and the Internet of Things; Springer, 3. Auflage, 2018.
• C. Haubelt, J. Teich: Digitale Hardware/Software-Systeme: Spezifikation und Verifikation; Springer 2010.

Last offered Wintersemester 2022
Planned for Wintersemester 2023
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS



Module Number

INFO-4312
Module Title

Design and Synthesis of Embedded Systems
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction German
Type of Exam

Oral examination (written exam if there are a large number of participants), successful exercises can result in a grade bonus

Content

Embedded systems have greatly influenced changes in data, communications, and automotive technologies over the past decade and have become an integral part of everyday life. This module covers the specific hardware aspects of Embedded Systems. Topics include: IC technologies for Embedded Systems, hardware design methods, modeling concepts and simulation methods using hardware description languages. Furthermore, communication between processes and modules within a chip is covered, different synchronization types are shown and on-chip bus systems from practice are presented. A main focus is on automated circuit synthesis from hardware and system description languages (VHDL, Verilog and SystemC) or software programming languages (C/C++). First, register transfer synthesis, logic synthesis, and technology mapping are addressed. Then, the concepts of architecture synthesis (high level synthesis) are introduced with the basic algorithms for clock cycle accurate scheduling and resource commitment. With the help of hardware description languages such as VHDL and Verilog, methods for modeling and simulating embedded systems are taught, which are applied and deepened accordingly through independent work in the exercises.

Objectives

Students will acquire specialist competences as well as basic concepts and technologies of modern embedded systems. The students are enabled to know and apply the principles of relevant hardware basic technologies, to master development techniques theoretically and practically and to be able to assess and optimise embedded systems. Furthermore, the students acquire an understanding of the methods and concepts of hardware description languages and automated circuit synthesis in digital hardware design. The exercises are worked on independently by the students in small groups and self-confidence, rhetorical skills and critical faculties are trained by demonstrating the results achieved.

Prerequisite for participation There are no specific prerequisites.
Lecturer Bringmann
Literature / Other

• O. Bringmann, W. Lange, M. Bogdan: Eingebettete Systeme: Entwurf, Synthese und Edge AI; De Gruyter Oldenbourg, 4. überarbeitete Auflage, 2022.

• J. Teich, C. Haubelt: Digitale Hardware/Software-Systeme: Synthese und
Optimierung; Springer 2007.

• G. De Micheli: Synthesis and Optimization of Digital Circuits. McGraw-
Hill, 1994.

Last offered Sommersemester 2022
Planned for Sommersemester 2023
Assigned Study Areas INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS



Module Number

INFO-4313
Module Title

Embedded Systems
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction English
Type of Exam

Presentation and written report

Content

Embedded systems have become a fundamental part of many technical systems
and are already integral part of everyday life, e.g. in mobile communications,
medical technology, consumer electronics, smart homes and autonomous vehicles
as well as in industrial automation and Internet of Things (IoT). The manifold
dependencies between software and the underlying hardware architecture
require a holistic approach in design, analysis, and verification of embedded systems.
This seminar examines modelling, analysis and verification approaches
for distributed embedded systems, highlights the latest research trends in computer
architecture and machine learning, and discusses their applicability in
future embedded systems. The students choose a seminar topic from the given
topic list, examine the topic in substance, and present the elaborated content
by an oral seminar presentation and a written report.

Objectives

Students are able to read, reflect, and examine the topic in substance upon current
research papers in the area of embedded systems and can critically assess
the contributions of a paper. They can present current research results to other
students and researchers and can lead research discussions. They can summarize
and evaluate the results of research papers in form of a oral presentation
and a written report.

Prerequisite for participation There are no specific prerequisites.
Lecturer Bringmann
Literature / Other

Will be announced in the pre-lecture meeting.

Last offered Sommersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDZ-SEM, ML-CS



Module Number

INFO-4315
Module Title

Advanced Topics in Embedded Systems
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Oral examination (written exam if there are a large number of participants)

Content

This lecture discusses current topics and trends in embedded system research
with special focus on design, analysis and verification of embedded systems and
Systems-on-Chip (SoCs). The lecture starts with an introduction into embedded
systems architectures and electronic system level design. Then, the latest
developments in analysis of non-functional properties like timing, power dissipation,
and energy consumption are discussed. The lectures on verification
addresses cyber-physical systems, safety verification, and robustness optimization
of machine-learning based embedded systems. The lecture finally covers
advanced hardware architectures for low-power implementation of deep learning
approaches in hardware. Between the lectures, practical exercises in form
of programming assignments will take place. The lecturers will present the
relevant basics as well as recent research results in each topic.

Objectives

Participants will acquire in-depth knowledge to different aspects in embedded
systems as well as the necessary skills to design, analyse, and verify embedded
systems under safety constraints. They will gain hands-on experience in
embedded system design in order to avoid common pitfalls. The students will
get a deeper practical understanding by working on topic-specific programming
assignments.

Prerequisite for participation INFO-4311 Modeling and Analysis of Embedded Systems,

INFO-4312 Design and Synthesis of Embedded Systems
Lecturer Bringmann
Literature / Other

Will be announced during the first lecture.

Last offered Sommersemester 2020
Planned for ---
Assigned Study Areas INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS



Module Number

INFO-4316
Module Title

Programming Ultra-Low Power Architectures
Lecture Type(s)

Practical Course
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction German
Type of Exam

Elaboration and presentation of the internship tasks

Content

This module provides an introduction to practical work with microcontrollers. The FRDM-KL25Z development platform based on a 32-bit ARM Cortex M0+ processor is used for this purpose. After a short introduction to the platform used, practical tasks are solved in teams of two. The practical tasks cover the following topics: Introduction to microcontroller programming, application execution time, performance analysis and optimization and memory requirements.

Objectives

The students can systematically develop software for embedded systems taking into account electrical power consumption and energy consumption. They know the entire development process from specification, through development, to debugging and documentation. Furthermore, the students are able to apply modern techniques of software-supported dynamic power management up to the programming of ultra-low-power applications. Emphasis is placed on teamwork, communication within and between groups, systematic problem solving and meeting deadlines. This promotes students' self-confidence, self-marketing skills and ability to deal with conflicts.

Prerequisite for participation INFO-4311 Modeling and Analysis of Embedded Systems
Lecturer Bringmann
Literature / Other

Literatur wird zu Beginn des Praktikums bekanntgegeben.

Last offered Sommersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4317
Module Title

Parallel Computer Architectures
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction German
Type of Exam

Mündliche Prüfung (bei großer Teilnehmerzahl Klausur), durch erfolgreiche Übungen kann ein Notenbonus erarbeitet werden.

Content

The module deals with the topic of parallel data processing from the perspective of computer architecture. Computer architecture concepts are presented, with the help of which parallelism can be exploited at various levels to increase performance. The module covers the following topics, among others: Machine instruction-level parallelism: superscalar technique, speculative execution, jump prediction, VLIW principle, multi-threaded instruction execution. Modern parallel computing concepts, memory-coupled parallel computers, symmetric multiprocessors, distributed shared memory multiprocessors, message-oriented parallel computers, multicore architectures, cache coherence protocols, performance evaluation of parallel computing systems, parallel programming models, interconnection networks (topology, routing), heterogeneous system architectures and GPGPUs.

Objectives

The students have extended technical competences in the area of modern computer architectures with a focus on parallel architectures, interconnection networks and heterogeneous systems. They know the advantages and disadvantages of the various parallel architectures as well as the difficulties that arise when programming such systems. This enables the students to apply appropriate programming concepts for parallel architectures in a situation-appropriate manner. In the exercises, the participants acquire a further understanding of the complexity of parallel processes and the resulting difficulties. Due to the independent work in small groups, the ability to work in a team and leadership qualities are particularly promoted.

Prerequisite for participation INF3341 Introduction to Computer Architecture
Lecturer Bringmann
Literature / Other

• J. L. Hennessy, D. A. Patterson: Computer Architecture: A Quantitive Approach, Morgan Kaufmann Publishers Inc, Elsevier, 6. Auflage, 2018.
• S. Pasricha, N. Dutt: On-Chip Communication Architectures; Morgan Kaufmann Publishers Inc., 2008.

Last offered Wintersemester 2022
Planned for Wintersemester 2023
Assigned Study Areas INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS



Module Number

INFO-4318
Module Title

Advanced Computer Architecture
Lecture Type(s)

Practical Course
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German
Type of Exam

Elaboration and presentation of the internship tasks

Content

The practical course deals with current research questions in computer architecture by means of practical tasks. This includes assignments on the following topics: Simulative Evaluation of Computer Architectures, Microarchitecture and Instruction Sets, Jump Prediction and Speculative Execution, Caches and Cache Coherence, Memory Organization and Interconnection Networks, and Computer Architecture Support for Operating Systems. In addition, an independently developed research project concludes of the practical course.

Objectives

This practical course enables students to evaluate current research questions in the field of computer architecture and to work on new questions independently. Through the practical handling of parallel architectures and parallelised applications, the students gain a further understanding of the complexity of parallel processes and the resulting difficulties. The independent processing of tasks enables the students to deal with methods and tools relevant to everyday life in science and business. Furthermore, they acquire the competence to program parallel computers efficiently and to apply the learned skills in depth within the framework of a research project. The tasks set in this module are worked on in small groups. In addition to teamwork, communication and conflict skills, this also trains the students' sense of responsibility.

Prerequisite for participation INFO-4317 Parallel Computer Architectures
Lecturer Bringmann
Literature / Other

• J. L. Hennessy, D. A. Patterson: Computer Architecture: A Quantitive Approach, Morgan Kaufmann Publishers Inc, Elsevier, 6. Auflage, 2018.

Last offered ---
Planned for ---
Assigned Study Areas INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS



Module Number

INFO-4194
Module Title

Behavior and Learning
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

This lecture builds on the available knowledge how animals and humans plan,
decide on, and control their behavior and how they progressively optimize and
adapt their behavior over time. Accordingly, algorithms are introduced for behavioral
decision making, control, optimization, and adaptation. In particular,
the lecture introduces spatial representations for behavioral control, forwardinverse
control models, including the learning of such representations and models.
Also the encoding and the learning of motor control primitives and motor
complexes is considered. Last but not least, self-motivated artificial systems
are considered that strive to maintain internal homeostasis and to maximize
information gain.

Objectives

Students know how intelligent behavior can be generated and learned in artificial
systems. They can apply reinforcement learning (RL), including hierarchical
RL, factored RL, and actor-critic approaches to the appropriate problems. Moreover,
they are aware of the contrast between model-free and model-based RL
approaches. They know about dynamic motion primitives and know how to optimize
them. Moreover, they know about Gaussian Mixture Models, including
how to learn and optimize them. They can implement information-gain driven
and self-motivated behavior and are aware of the exploration-exploitation
dilemma. Moreover, they are aware of model-predictive control, of options to
learn suitable model-predictive structures, and of options to suitably abstract
such structures. Finally, they know how sensorimotor-grounded spatiotemporal
representations can be learned, stored as episodic memory units, and can be
abstracted into cognitive maps, enabling model-based RL.

Prerequisite for participation There are no specific prerequisites.
Lecturer Butz
Literature / Other

Literatur / Literature:
Wird in der Veranstaltung ausgegeben (Buchkapitel und Artikel in Englisch). / Will be supplied (book chapters and papers in English).

Voraussetzungen / Prerequisites:
Vorkenntnisse in maschinellem Lernen, Künstlichen Neuronalen Netzen, Deep Learning oder Künstlicher Intelligenz sind notwenig. / Knowledge about machine learning, artificial neural networks, deep learning, or artificial intelligence is required.

Last offered ---
Planned for ---
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-HCI, MEDI-INFO, MEDI-MEDI, ML-CS



Module Number

INFO-4210
Module Title

Recurrent and Generative Artificial Neural Networks
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

Advanced ANN topics. First, revisiting backpropagation and backpropagation
through time; then: Advanced Recurrent Neural Networks (LSTM, GRU);
Very Deep Learning and Generative Adversarial Networks; Spatial and Temporal
Convolution; Reservoir Computing; Neuroevolution; Attention and Routing
Networks; Autoencoders and Restricted Boltzmann Machines; Gain Fields and
Switching Networks; Latent Space Visualization techniques; Generative Inference

Objectives

Students know about and how to apply generative and typically recurrent artificial
neural networks in various domains including data classification, image
recognition, language processing, spatially-invariant recognition, spatial transformations,
and spatial mappings. They can apply complex, generative artificial
neural networks from scratch as well as with available tools. They know how to
optimize weights and network structures by means of gradient descent as well
as by alternative methods. They can use complex recurrent network structures
to selectively process aspects of the data. They know how to apply generative
networks as model-predictive neural controllers and as well as long-range
temporal predictors. They can combine retrospective latent state and motor
inference techniques with prospective motor control.

Prerequisite for participation There are no specific prerequisites.
Lecturer Butz
Literature / Other

Literatur / Literature:
Wird in der Veranstaltung ausgegeben (Buchkapitel und Artikel in Englisch). / Will be supplied (book chapters and papers in English).

Voraussetzungen / Prerequisites:
Vorkenntnisse in maschinellem Lernen, Künstlichen Neuronalen Netzen, Deep Learning oder Künstlicher Intelligenz sind notwenig. / Knowledge about machine learning, artificial neural networks, deep learning, or artificial intelligence is required.

Last offered Sommersemester 2022
Planned for ---
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, ML-CS, ML-DIV



Module Number

INFO-4211
Module Title

Avatars in Virtual Realities
Lecture Type(s)

Practical Course
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Final Project Presentation and Report

Content

In this project-oriented practical course, students learn how to design realistic,
interesting, behaving avatars in virtual realities. Typically the focus lies
in developing user interfaces, and new options for interacting with the VR
and acting upon objects or other entities within the VR. Alternatively, experimental
setups will be programmed and optimized in order to run real-world
psychological and evaluative experiments in which users control avatars in VR.

Objectives

Students know how to work with virtual realities (VRs) and how to develop
animated, autonomous avatars in these environments. They are able to create
and use suitable interfaces to enable users to effectively interact with VRs and
control avatars within.

Prerequisite for participation There are no specific prerequisites.
Lecturer Butz
Literature / Other

keine / Solid Knowledge in Programming. General knowledge about simulation software.

Last offered -
Planned for -
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-HCI, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDI-PRAX, ML-CS



Module Number

INFO-4212
Module Title

Artificial Neural networks
Lecture Type(s)

Practical Course
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Final Project Presentation and Report

Content

Programming enhanced functionalities in ANN Software, evaluating performance,
analyzing the system.

Objectives

Know how to work with, implement, and enhance complex artificial neural
networks.

Prerequisite for participation There are no specific prerequisites.
Lecturer Butz
Literature / Other

keine / Solid Knowledge in Programming. Knowledge about artificial neural networks
or machine learning.

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-PRAX, MEDI-VIS, ML-CS



Module Number

INFO-4213
Module Title

Advanced Artificial Neural Networks Project
Lecture Type(s)

Practical Course
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Final Project Presentation and Report

Content

Working with ANN Software, evaluating performance, & analyzing the system.

Objectives

Know how to evaluate, program, and analyze artificial neural networks.

Prerequisite for participation There are no specific prerequisites.
Lecturer Butz
Literature / Other

keine / Solid Knowledge in Programming. Knowledge about artificial neural networks
or machine learning.

Last offered -
Planned for -
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, ML-CS, ML-DIV



Module Number

INFO-4214 (MKOGP3)
Module Title

Cognitive Modeling
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction English
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

Cognitive models covering learning, action and perception are presented and
discussed, including descriptive, qualitative, quantitative and neural models. In
addition, parameter optimization as well as techniques to compare models and
to interpret and evaluate model parameters are introduced. All techniques are
shown in the context of concrete models of cognitive processes. Moreover, the
necessary statistical methods are introduced in a practical, application-oriented
manner.

Objectives

Students know the most important principles and techniques of cognitive modeling.
They know how to model cognitive processes, mechanisms, and learning
at different levels of complexity. They can apply various cognitive models and
modeling approaches in a goal-directed manner. Moreover, they can evaluate,
compare, and contrast different modeling approaches as well as modeling
results. They are able to judge whether a model is falsifiable and they know
how to validate and interpret cognitive models. Finally, they can use statistical
methods to quantitatively compare different cognitive models.

Prerequisite for participation There are no specific prerequisites.
Lecturer Butz, Wichmann
Literature / Other

Book: S. Lewandowsky & S. Farrell (2011). Computational Modeling in Cognition.
Additional papers and book chapters will be supplied. Introductory course knowledge about machine learning, artificial neural networks, robotics, cognitive architectures, or artificial intelligence is required.

Last offered Sommersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-HCI, MEDI-INFO, MEDI-MEDI, ML-CS



Module Number

BIOINF4998 (entspricht BIO-4998)
Module Title

Research Project Bioinformatics
Lecture Type(s)

Research Project
ECTS 9
Work load
- Contact time
- Self study
Work load:
270 h
Class time:
90 h / 6 SWS
Self study:
180 h
Duration 1 Semester
Frequency Each semester
Language of instruction German and English
Type of Exam

Presentation and written report (either as a scientific paper or as a report (15-20 pages)

Content

The research project aims to deepen theoretical and practical knowledge in a
specific area of bioinformatics. Students participate in a research project with
the thematic focus of the research group.

Objectives

Bioinformatics research project: The students
• gain insight into scientific work,
• learn how to independently pursue a research question,
• learn to independently identify and compile scientific literature for the research question to be worked on,
• are able to work in a team in a scientific international environment,
• deepen their problem-solving skills.

Prerequisite for participation There are no specific prerequisites.
Lecturer Dozenten der Bioinformatik
Literature / Other

Wissenschaftliche Literatur/Veröffentlichungen relevant für das zu bearbeitende Forschungsthema / Exzellente akademische Noten im Master Bioinformatik. Es gibt nur wenige Forschungsprojekte, die semesterweise angeboten werden. Eine schriftliche Bewerbung, incl. Motivationsschreiben, CV und Transcript of Records sind an den Arbeitsgruppenleiter des angebotenen Forschungsprojektes zu schicken.

Last offered unknown
Planned for currently not planned
Assigned Study Areas BIO-BIO



Module Number

INFO-4998
Module Title

Research Project Computer Science
Lecture Type(s)

Research Project
ECTS 9
Work load
- Contact time
- Self study
Work load:
270 h
Class time:
90 h / 6 SWS
Self study:
180 h
Duration 1 Semester
Frequency Each semester
Language of instruction German and English
Type of Exam

Presentation and written report

Content

The research project serves to deepen theoretical and practical knowledge in a specific area of practical / theoretical / technical computer science. Students work on a research project of the thematic focus of the research group and are ideally involved in the production of a scientific publication in the topic area.

Objectives

The students
- gain insight into scientific work,
- learn how to independently pursue a research question,
- learn how to independently identify and compile scientific literature for the research question to be addressed,
- are able to work in a team in a scientific international environment,
- deepen their problem-solving skills,
- are able to give a scientific presentation.

Prerequisite for participation There are no specific prerequisites.
Lecturer Dozenten der Informatik
Literature / Other

Wissenschaftliche Literatur/relevante Veröffentlichungen für das zu bearbeitende Forschungsthema.

Last offered ---
Planned for ---
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

MEDI-4998
Module Title

Research Project Media Informatics
Lecture Type(s)

Research Project
ECTS 9
Work load
- Contact time
- Self study
Work load:
270 h
Class time:
90 h / 6 SWS
Self study:
180 h
Duration 1 Semester
Frequency Each semester
Language of instruction German and English
Type of Exam

Written elaboration and presentation based on it

Content

The research project serves to deepen theoretical and practical knowledge in a specific area of media informatics. Students work on a research project of the thematic focus of the research group and are preferably involved in the production of a scientific publication in the thematic area.

Objectives

Research project in media informatics: The students
- gain insight into scientific work,
- learn how to independently pursue a research question,
- learn how to independently identify and compile scientific literature for the research question to be addressed,
- are able to work in a team in a scientific international environment,
- deepen their problem-solving skills,
- are able to give a scientific presentation.

Prerequisite for participation There are no specific prerequisites.
Lecturer Dozenten der Medieninformatik
Literature / Other

Wissenschaftliche Literatur/Veröffentlichungen relevant für das zu bearbeitende Forschungsthema

Last offered ---
Planned for ---
Assigned Study Areas MEDI-PRAX



Module Number

MEDZ-4998
Module Title

Research Project Medical Informatics
Lecture Type(s)

Research Project
ECTS 9
Work load
- Contact time
- Self study
Work load:
270 h
Class time:
90 h / 6 SWS
Self study:
180 h
Duration 1 Semester
Frequency Each semester
Language of instruction German and English
Type of Exam

Written elaboration and presentation based on it

Content

The research project serves to deepen theoretical and practical knowledge in a specific area of medical informatics. Students work on a research project with the thematic focus of the research group. Students acquire knowledge in the current research environment of medical informatics and in independent research including the necessary documentation and handling of primary research data. Thus, the course leads directly to a research-related master thesis.

Objectives

The students
- gain insight into scientific work,
- learn how to independently pursue a research question,
- learn how to independently identify and compile scientific literature for the research question to be addressed,
- are able to work in a team in a scientific international environment,
- deepen their problem-solving skills,
- are able to give a scientific presentation.

Prerequisite for participation There are no specific prerequisites.
Lecturer Dozenten der Medizin- und Bioinformatik
Literature / Other

Wissenschaftliche Literatur/Veröffentlichungen relevant für das zu bearbeitende Forschungsthema / Exzellente akademische Noten im Master Medizininformatik. Es gibt nur wenige Forschungsprojekte, die semesterweise angeboten werden. Eine schriftliche Bewerbung, incl. Motivationsschreiben, CV und Transcript of Records sind an die Arbeitsgruppenleiter*in des angebotenen Forschungsprojektes zu schicken.

Last offered ---
Planned for ---
Assigned Study Areas MEDZ-RES



Module Number

INFO-4413
Module Title

Parameterized Algorithms
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

This module gives an introduction to the theory of parameterized algorithms and parameterized complexity theory. The focus is on different methods and techniques for developing parameterized algorithms. This module introduces solution approaches to NP complete problems, parameterized algorithms, and problem kernels. Different methods and techniques, such as data reduction and problem kernels, depth-constrained search trees, dynamic programming, tree decompositions, iterative compression, color coding, and linear programming, are introduced. From the area of parametrized complexity theory, parametrized reduction, the class FPT and Hardness classes are treated.

Objectives

Students have basic knowledge of parameterised algorithms and parameterised complexity and can assess and determine the difficulty of NP-complete problems and algorithms for solving them exactly. They know different methods and techniques for designing parameterised algorithms. They can solve different problems with the repertoire of methods presented as well as creatively develop parameterised algorithms independently. The students can distinguish between different strategies for the design of parameterised algorithms and apply these adapted to the problem. The students are able to critically evaluate a parameterised algorithm. They recognise advantages and disadvantages of this approach and can place it in the context of other methods for solving NP-complete problems such as heuristics, approximation algorithms, randomised algorithms.

Prerequisite for participation There are no specific prerequisites.
Lecturer Dorn
Literature / Other

Rolf Niedermeier: Invitation to Fixed-Parameter Algorithms, Oxford University Press.
Rodney G. Downey, Michael R. Fellows: Parameterized Complexity, Springer-Verlag, 1999.
Jörg Flum, Martin Grohe: Parameterized Complexity Theory, Springer-Verlag, 2006.

Last offered ---
Planned for ---
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4414
Module Title

Parameterized Algorithms
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

To be announced.

Content

The seminar includes the elaboration of written sources on topics from the field of Parametrized Algorithms and Complexity under supervision. Presentation and the written summary conclude the seminar work in each case. Active participation in each session is an important part of the seminar.

Objectives

The students can independently work out and understand an extended and complex subject from the field of Parametrised Algorithms and Parametrised Complexity from a written source and present it in the form of a lecture and also represent it in a discussion in front of a plenum. In addition to the oral presentation, they can present and summarise the elaborated topic in writing.

Prerequisite for participation INFO-4413 Parameterized Algorithms
Lecturer Dorn
Literature / Other

Rolf Niedermeier: Invitation to Fixed-Parameter Algorithms, Oxford University Press, und Weitere (wechselnd).

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS



Module Number

INFO-4417
Module Title

Parameterized Algorithms and Complexity
Lecture Type(s)

Lecture, Tutorial
ECTS 9
Work load
- Contact time
- Self study
Work load:
270 h
Class time:
90 h / 6 SWS
Self study:
180 h
Duration 1 Semester
Frequency Irregular
Language of instruction German
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

This module gives an extended introduction to the theory of parameterized algorithms and parameterized complexity theory. The focus is on different methods and techniques for developing parameterized algorithms. This module introduces solution approaches to NP-complete problems, parameterized algorithms, and problem kernels. Different methods and techniques, such as data reduction and problem kernels, depth-constrained search trees, dynamic programming, tree decompositions, iterative compression, color coding, and linear programming, are introduced. From the area of parametrized complexity theory, parametrized reduction, the class FPT and hardness classes are treated. In addition, issues and trends of current research as well as applications in various fields such as bioinformatics, artificial intelligence, or Computational Social Choice are presented.

Objectives

Students have basic knowledge of parameterised algorithms and parameterised complexity and can assess and determine the difficulty of NP-complete problems and algorithms for solving them exactly. They know different methods and techniques for designing parameterised algorithms. They can solve different problems with the repertoire of methods presented as well as creatively develop parameterised algorithms independently. The students can distinguish between different strategies for the design of parameterised algorithms and apply these adapted to the problem. They are able to critically evaluate a parameterised algorithm. They recognise advantages and disadvantages of this approach and can classify it in the context of other methods for solving NP-complete problems such as heuristics, approximation algorithms, randomised algorithms. In addition, the students are familiar with issues of current research and know application examples and case studies.

Prerequisite for participation There are no specific prerequisites.
Lecturer Dorn
Literature / Other

Rolf Niedermeier: Invitation to Fixed-Parameter Algorithms, Oxford University Press, 2006.
Rodney G. Downey, Michael R. Fellows: Parameterized Complexity, Springer-Verlag, 1999.
Jörg Flum, Martin Grohe: Parameterized Complexity Theory, Springer-Verlag, 2006.

Last offered ---
Planned for ---
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4999
Module Title

Seminar on Selected Topics in Practical Computer Science
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Presentation and written report

Content

In this module we discuss advanced topics in the area of practical computer science.

Note on the summer term 2024:
This term, the seminar "Software-Architektur-Stile und -Patterns" (Instructor: PD Holger Gast) will be offered.

Objectives

Students get to know current topics in the area of practical computer science. Students will be able to acquire knowledge about state-of-the-art topics in practical computer science through comprehensive literature search. Students will not only have improved their study and reading skills, but will also have enhanced their capability of working independently. The teaching method in this seminar aims at boosting the students’ confidence (oral presentation), and at enhancing their communication skills and enabling them to accept criticism (discussion session following their presentation.

Prerequisite for participation There are no specific prerequisites.
Lecturer Alle Dozenten
Literature / Other

Will be handed out in the course

Last offered unknown
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS



Module Number

MEDZ-4310
Module Title

Selected Topics in Medical Informatics
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Oral examination (written exam if there are a large number of participants)

Content

The course deepens the crossover between medicine and bioinformatics and forms a core course in the research-oriented MSc Medical Informatics.

Objectives

The students recognise the connection between medicine and bioinformatics on the basis of current research fields.

Prerequisite for participation There are no specific prerequisites.
Lecturer Dozenten der Medizin- und Bioinformatik
Literature / Other

-

Last offered ---
Planned for ---
Assigned Study Areas MEDZ-BIOMED, MEDZ-RES



Module Number

MEDZ-4320
Module Title

Selected Topics in Medical Informatics
Lecture Type(s)

Lecture
ECTS 9
Work load
- Contact time
- Self study
Work load:
270 h
Class time:
90 h / 6 SWS
Self study:
180 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Oral Test

Content

The course deepens the crossover between medicine and bioinformatics and forms a core course in the research-oriented MSc Medical Informatics.

Objectives

Based on current research fields, students recognise the connection between medical informatics and bioinformatics.

Prerequisite for participation There are no specific prerequisites.
Lecturer Dozenten der Medizin- und Bioinformatik
Literature / Other

-

Last offered ---
Planned for ---
Assigned Study Areas MEDZ-BIOMED, MEDZ-RES



Module Number

BIOINF4393 (entspricht BIO-4393)
Module Title

Mathematical Methods in (Medical) Systems Biology
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written elaboration and presentation based on it

Content

This seminar focuses on the key concepts of computational, mathematical, and statistical models used in cancer and cancer medicine, and helps students learn how mathematical models help us understand cancer progression. The technical articles used in this seminar provide the biological background and describe the development of both classical mathematical models and more recent representations of biological processes. Therefore, students will examine existing mathematical models and learn to deal with the key parameters involved in modeling and the impact of changes in these parameters to discuss how they relate to treatment, prevention, or policy making in general. Through discussions of published work, students will learn how to critically evaluate a modeling effort and how to communicate modeling results to readers of scientific journals. The seminar is useful for students who use experimental techniques as an approach in the laboratory and want to use computational modeling as a tool to gain a deeper understanding of experiments.

Objectives

Students learn to apply methods of mathematical modelling to systems biology models. This includes
- Have knowledge of the basic concepts of biological networks, the basic structure of systems biology models and the analysis of complex biological systems using mathematical and scientific skills through scientific publications.
- Have knowledge and understanding of how to formulate mathematical models of cellular processes and biochemical reactions and estimate their model parameters.
- Know the main areas in cancer where mathematical modelling has contributed to our understanding of how to read a mathematical modelling paper in all its aspects (methods, results, discussion).

Prerequisite for participation INFM1010 Mathematics for Computer Science 1: Analysis,

INFM1020 Mathematics for Computer Science 2: Linear Algebra,

INFM2010 Mathematics for Computer Science 3: Advanced Topics
Lecturer Mostolizadeh
Literature / Other

Originalarbeiten und zusätzliche Materialien werden im Seminar ausgegeben.

Last offered unknown
Planned for currently not planned
Assigned Study Areas BIO-BIO, BIO-SEM, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-MEDTECH, MEDZ-SEM, ML-CS



Module Number

BIOINF4394 (entspricht BIO-4394)
Module Title

Systems Biology II
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction English
Type of Exam

Written exam (oral exam with a small number of participants), practice certificate as an exam requirement. Practice points can be included as bonus points in the exam evaluation.

Content

This application-oriented course imparts essential knowledge on the dynamic modeling of biological systems. This opens up numerous application areas, such as optimizing biotechnological processes, personalized medicine, preclinical studies, and understanding current systems biology research. In addition, students learn to work with the programming environment Tellurium, which is based on the Python programming language and brings with it the declarative systems biology modeling language called “Antimony.”

Students will learn the basic approach to building biochemical reaction models and concepts for analyzing dynamic network states. Data sources and forms of representation for the models will be covered. Emphasis is placed on physical constraints and implicit assumptions, such as conservation of mass, types of biochemical reactions, principles of enzyme catalysis, application and derivation of kinetic equations, open and closed systems and the influence of reversible reactions on the overall system, and processes occurring on different time scales to obtain plausible models.

Furthermore, energy conservation, the influence of cofactors and redox potentials, and regulatory mechanisms in biochemical systems are considered. Students learn how to classify the correctness of simulation results by estimating the magnitudes of cellular components. Students will gain an overview of numerical methods relevant to simulation and learn how to simulate models dynamically. Suitable graphical representations for the analysis of simulation results are discussed. Finally, the principles learned are applied to selected metabolic pathways, and their coupling concerning the cellular scale is discussed.

The content does not build directly on the lecture Systems Biology I so this course can be attended independently.

Objectives

Students learn to apply methods of mathematical modeling to systems biology models.
This includes
- the creation of models of biochemical reaction networks,
- the simulation and analysis of the dynamic responses of these models,
- as well as basic programming techniques for solving systems biology problems.

Prerequisite for participation There are no specific prerequisites.
Lecturer Dräger
Literature / Other

1. Bernhard Ø. Palsson 2011. Systems Biology: Simulation of Dynamic Network
States. Cambridge University Press, New York, ISBN 978-1-107-
00159-6.
2. David S. Goodsell. 2009. The Machinery of Life. 2. Ausgabe, Springer-
Verlag, ISBN 978-0387849249.
3. Jan Koolman und Klaus-Heinrich Roehm. Color Atlas of Biochemistry.
2. Ausgabe, Thieme, 2005.

Last offered Sommersemester 2022
Planned for Wintersemester 2023
Assigned Study Areas BIO-BIO, MEDZ-BIOMED, MEDZ-RES



Module Number

INFO-4250
Module Title

Information Processing for Perception and Action
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

Will be announced at beginning of semester

Content

Humans as well as complex technical systems process sensory information to
interact with the environment. These actions have consequences which (again)
create sensory events that can be processed and used to improve the interaction
with the environment. We will discuss advanced topics of this full ’perception–
action’ loop; in humans as well as in technical systems. A special focus will be
on the experimental literature from the Cognitive– and Neurosciences and on
advanced statistical methods.

Objectives

Students will know current views on biological information processing and on
the interaction of humans with technical systems. They will also learn and understand
advanced statistical and empirical methods that were used to generate
this knowledge. This expertise will help them to apply their knowledge in interdisciplinary
working environements, whenever empirical studies on human
performance and actions are required.

Prerequisite for participation There are no specific prerequisites.
Lecturer Franz
Literature / Other

Wird zu Beginn des Semesters bekanntgegeben / Will be announced at beginning
of semester, / No formal requirements, but students should have a good background in statistics and should have attended introductory/mid–level courses in Cognitive
Science/Neuroscience.

Last offered -
Planned for -
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-HCI, MEDI-INFO, MEDI-MEDI, MEDZ-SEM, ML-CS



Module Number

INFO-4177
Module Title

Intelligent Systems II - Learning in Computer Vision
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

Graphical Models; Bayesian Belief Networks; Markov Random Fields; Conditional Random Fields; Learning of Structured Variables; Bayesian Decision Theory; Loss-based Learning; Parameter Learning in Graphical Models; Structured Support Vector Machines; Exact and Approximate Inference Methods; Applications in Image Processing; Segmentation; Human Pose Estimation; Image Denoising; Stereo; Object Detection.

Objectives

Students learn how complicated statistical relationships can be represented with the help of graphical models. Concrete and current problems from the fields of image processing and image understanding are solved. Various learning methods make it possible to automatically set data-driven parameters and evaluate the performance achieved.

Prerequisite for participation There are no specific prerequisites.
Lecturer Gehler, Lensch, MPI
Literature / Other

Vorlesungsfolien werden bereitgestellt

Last offered Sommersemester 2020
Planned for ---
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, ML-CS, ML-DIV



Module Number

INFO-4141
Module Title

Implementation of Relational Database Systems (DB2)
Lecture Type(s)

Lecture, Tutorial
ECTS 9
Work load
- Contact time
- Self study
Work load:
270 h
Class time:
90 h / 6 SWS
Self study:
180 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

Written exam (oral exam with a small number of participants), exercise points
can be included as bonus points in the exam evaluation

Content

Internals of PostgreSQL and MonetDB, secondary storage access and data layout, index structures (B+ trees, hashes), multidimensional index structures, sorting methods on secondary storage, query evaluation, plan generation and optimization for SQL, transactions (ACID principle).

Objectives

The role and relevance of the internals of a database system are clarified and analysed. Throughout the semester, we contrast PostgreSQL and MonetDB so that students can assess the suitability of disk-based and main memory-based database technology for concrete applications. Students know how to link this new knowledge with the concepts of the lecture "DB1". The students understand which basic parameters and algorithms enable efficient database operation and can optimise them for concrete applications. In doing so, the topic is treated in a depth that provides students with reading and learning skills as well as trains discipline and precision.

Prerequisite for participation There are no specific prerequisites.
Lecturer Grust
Literature / Other

• Ramakrishnan / Gehrke: Database Management Systems
• Heuer / Saake: Datenbanken: Implementierungstechniken
• Relationale Datenbanksysteme (Software und Manuals): PostgreSQL,
MonetDB
• Klassische und aktuelle Forschungsartikel zum Thema

Last offered Sommersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4142
Module Title

Database Systems and Modern CPU Architecture
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

Written exam (oral exam with a small number of participants), exercise points
can be included as bonus points in the exam evaluation

Content

CPU architectures, pipelining, parallelism, multi-scale CPUs, pipelining and query evaluation, CPU caches, cache aware database architecture, main memory databases (internals and practical use).

Objectives

Students understand a database system as a synthesis of CPU/computer architecture and actual database architecture. They can evaluate existing database architectures with regard to their suitability for execution on a given computer architecture. This module connects the worlds of CPUs (instruction level) and database systems (query processor) and thus promotes system understanding across many architectural levels.

Prerequisite for participation INFO-4141 Implementation of Relational Database Systems (DB2)
Lecturer Grust
Literature / Other

• Hennessy / Patterson: Computer Architecture - A Quantitative Approach
• CPU-Simulatoren und Hauptspeicher-Datenbanksysteme (Software und
Manuals), etwa MonetDB, VectorWise, HyPer, Umbra, kdb+
• Aktuelle Forschungsartikel zum Thema

Last offered ---
Planned for ---
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS



Module Number

INFO-4147
Module Title

Declarative Database Languages
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

Written exam (oral exam with a small number of participants), exercise points
can be included as bonus points in the exam evaluation

Content

Semantics and internal representation of SQL (e.g., comprehensions), compilation of SQL, database languages for non-relational data, new paradigms for data-intensive programming, interaction of databases and programming environments, compilation of programming language constructs for execution on database systems

Objectives

The students know compilation techniques for the database languages covered. References to classical compiler construction and the necessity of new translation methods are recognised. The students know the central concept of impedance mismatch, which determines the entire subject area. The resulting problems are analysed and alternative solutions can be assessed in terms of usability and efficiency. References to functional programming languages (semantics and translation methods) can be recognised and exploited. The topic is treated in a depth that provides the students with reading and learning skills and trains discipline and precision.

Prerequisite for participation INF3131 Introduction to Relational Database Systems (DB1),

INF3182 Compiler Construction
Lecturer Grust
Literature / Other

• Compiler / Interpreter und Datenbanksysteme (Software und Manuals)
• Literatur zu deklarativen und funktionalen Programmiersprachen
• Aktuelle Forschungsartikel zum Thema

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4149
Module Title

Selected Topics in Database Systems
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

Written exam (oral exam with a small number of participants), exercise points
can be included as bonus points in the exam evaluation

Content

Changing in-depth topics from the subfields of the research field of database systems. Use of database systems for the realization of demanding applications (Advanced SQL).

Objectives

Students have knowledge of research methodology in the field of database systems. The focus is primarily on the use of SQL as a database language, its efficient translation, as well as its use for the realisation of very complex applications. The participants are prepared for writing scientific papers, especially in sub-areas of the research field of database systems. The students can prepare specifically for Master's theses and research projects.

Prerequisite for participation INF3131 Introduction to Relational Database Systems (DB1)
Lecturer Grust
Literature / Other

Klassische und aktuelle Forschungsliteratur zum Themengebiet.

Last offered unknown
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4663
Module Title

Advanced Topics in Database Systems
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Presentation

Content

The seminar includes the elaboration of sources under supervision on advanced topics in the field of database technology. The presentation and the written summary conclude the seminar work in each case. Active participation in the individual sessions is an important part of the seminar.

Objectives

The students can independently work out and understand an extended and complex issue in the field of database systems from original (English) sources and present it in the form of a presentation and also represent it in a discussion in front of a plenum. The use of different presentation techniques is weighed up, trained and practised in the plenary. As listeners, the participants are able to give their fellow students critical but fair feedback on the content and formal aspects of the presentation. In addition to the oral presentation, they can present the developed topic in writing and summarise it in the form of a short scientific article.

Prerequisite for participation INF3131 Introduction to Relational Database Systems (DB1)
Lecturer Grust
Literature / Other

Wechselnde Original-Literatur aus dem Forschungsfeld der Datenbanksysteme

Last offered Wintersemester 2022
Planned for Wintersemester 2023
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS



Module Number

INFO-4664
Module Title

Data and Business Analytics
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction German
Type of Exam

Presentation 40%, elaboration 40%, participation in the discussions 20%

Content

The seminar covers theoretical foundations as well as practical and work-related implementation of Real-World applications in science and economics.

The selection of the specific topics depends on the interests and knowledge of the students. The topics will be distributed in a preliminary meeting (in case of two many interests they will be drawn).

Objectives

TBD

Prerequisite for participation There are no specific prerequisites.
Lecturer Huber
Literature / Other

-

Last offered Sommersemester 2021
Planned for Sommersemester 2023
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-HCI, MEDI-INFO, MEDI-MEDI, MEDZ-SEM, ML-CS



Module Number

BIOINF4241 (entspricht BIO-4241)
Module Title

App Design in Bioinformatics
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written Test

Content

inhalt 4241

Objectives

goals 4241

Prerequisite for participation There are no specific prerequisites.
Lecturer Huson
Literature / Other

-

Last offered unknown
Planned for currently not planned
Assigned Study Areas BIO-BIO, INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-HCI, MEDI-INFO, MEDI-MEDI, MEDZ-BIOMED, ML-CS



Module Number

BIOINF4242 (entspricht BIO-4242)
Module Title

Advanced Topics in Bioinformatics
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Each semester
Language of instruction English
Type of Exam

Programming project

Content

In this course, we study the latest features of Java to address challenging programming
problems in bioinformatics. Topics include JavaFx, two- and thirddimensional
graphics, properties and bindings, animation, concurrent programming
and webprogramming. We will build a full-featured, interactive bioinformatics
program.

Objectives

The students are able to design and implement a fully featured bioinformatics
program. They are able to analyze a computational problem and to develop
an appropriate solution. They are aware of both the possibilities and the limitations
of the application of Java to solve computational tasks. They are able
to analyse problems on a scientific level and summarise them in writing. In
particular, a high degree of intrinsic motivation and personal responsibility is
encouraged.

Prerequisite for participation There are no specific prerequisites.
Lecturer Huson
Literature / Other

Programming and bioinformatics literature

Last offered Sommersemester 2022
Planned for Sommersemester 2023
Assigned Study Areas BIO-BIO, INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, ML-CS



Module Number

BIOINF4240 (entspricht BIO-4240)
Module Title

Bioinformatics Tools
Lecture Type(s)

Practical Course
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Each semester
Language of instruction English
Type of Exam

The final grade is based on performance, a written report on each day of the practical course, and one or two short oral presentations.

Content

In this practical course, students work on a mini research project in the area of
genomics, metagenomics or phylogenetics. Working in teams, the participants
use state-of-the-art bioinformatics tools to address a series of typical computational
questions. During the course, students read up on different methods and
introduce the methods to each other in short presentations.

Objectives

Students will gain practical experience in application of bioinformatics software
for analyzing NGS data in the context of genomics, metagenomics or phylogenetics.
They will be able to use libraries and frameworks, and will acquire
knowledge or extend their knowledge of Java and Python. By working together
in groups, students obtain teamwork and collaboration skills, and they will
learn about project organization and presentation techniques. Students will
know about the strengths and weaknesses and about the limitations of various
methods for molecular sequence data, and will be able to describe and evaluate
these methods.

Prerequisite for participation There are no specific prerequisites.
Lecturer Huson
Literature / Other

Scientific publications

Last offered Sommersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas BIO-PRAK



Module Number

BIOINF4311 (entspricht BIO-4311)
Module Title

Microbiome Analysis
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written or oral exam

Content

This course provides an in-depth introduction to microbiome analysis. Topics
include: Sequencing technologies, Community profiling using the SSU rRNA
gene, Community profiling using shotgun sequencing, Alignment-free and
alignment-based taxonomic profiling, Functional analysis and profiling, Sample
comparison and time-series analysis.

Objectives

The students are familiar with recent bioinformatics findings on microbiome
analysis. They can formulate the challenges of microbiome analysis for bioinformatics.
They know algorithms for taxonomic and functional analysis of
microbiome sequencing data, statistical methods for comparison and methods
for community profiling using 16S sequences. Students can analyse microbiome
sequencing data and perform profiling and comparison. They are aware of
both the possibilities and the limitations of different methods in this subfield
of bioinformatics. They are able to analyse problems on a scientific level and
summarise them in writing. In particular, a high degree of intrinsic motivation
and personal responsibility is encouraged.

Prerequisite for participation There are no specific prerequisites.
Lecturer Huson
Literature / Other

Lecture notes and scientific publications

Last offered Sommersemester 2021
Planned for currently not planned
Assigned Study Areas BIO-BIO, INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, ML-CS



Module Number

BIOINF4322 (entspricht BIO-4322)
Module Title

Metagenomics
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Oral Presentation (about 30 minutes) and written elaboration (approx. 10 pages), leading the discussion once

Content

In this seminar, we look at current research topics in the area of microbiome
analysis, such as metagenomics, meta-transcriptomics and meta-proteomics.
We will focus, to a degree, on the human microbiome. We provide a number of
topics and associated publications to choose from and each participant delivers
an oral presentation and a writeup of their chosen topic.

Objectives

The students can independently work with supervision on a challenging topic
through systematic research. They summarize, assess, classify and scientifically
correctly represent and present concepts and methods in the context of microbiome
analysis. On the one hand, students will obtain an overview of modern
knowledge in the field of microbiome analysis. On the other hand, students will
know that there are still many open research questions in this field. By studying
current articles, the students have not only improved their reading and learning
skills, but also their personal responsibility. The form of learning used in the seminar
is intended to help the students to develop self-confidence (presentation)
and the ability to criticise and communicate (subsequent discussion).

Prerequisite for participation There are no specific prerequisites.
Lecturer Huson
Literature / Other

Scientific publications

Last offered unknown
Planned for currently not planned
Assigned Study Areas BIO-BIO, BIO-SEM, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS



Module Number

BIOINF4361 (entspricht BIO-4361)
Module Title

Advanced Sequence Analysis
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction English
Type of Exam

Written Test

Content

inhalt bio4361

Objectives

goals bio 4361

Prerequisite for participation There are no specific prerequisites.
Lecturer Huson
Literature / Other

-

Last offered unknown
Planned for currently not planned
Assigned Study Areas BIO-BIO, INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, ML-CS



Module Number

BIOINF4362 (entspricht BIO-4362)
Module Title

Algorithms in Bioinformatics
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Oral Presentation (about 30 minutes) and written elaboration (approx. 10 pages), leading the discussion once

Content

In this seminar we look at current research topics in bioinformatics, for example,
fast alignment methods or single sequencing assembly. We provide a number of
topics and associated publications to choose from and each participant delivers
an oral presentation and a writeup of their chosen topic.

Objectives

The students can independently work with supervision on a challenging topic
through systematic research. Students gain experience in giving a technical
presentation and producing a technical writeup in bioinformatics. They summarize,
assess, classify, scientifically correctly represent and present concepts
and methods of algorithms in bioinformatics. On the one hand, the students
will get an overview of the state of the art of algorithms in bionformatics. On
the other hand, they will know that there are still many open research questions
in this field. By studying current articles, the students have not only improved
their reading and learning skills, but also their personal responsibility. The
form of learning used in the seminar is intended to help the students to develop
self-confidence (presentation) and the ability to criticise and communicate
(subsequent discussion).

Prerequisite for participation There are no specific prerequisites.
Lecturer Huson
Literature / Other

Scientific publications

Last offered unknown
Planned for currently not planned
Assigned Study Areas BIO-BIO, BIO-SEM, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS



Module Number

BIOINF4110 (entspricht BIO-4110)
Module Title

Sequence Bioinformatics
Lecture Type(s)

Lecture, Tutorial
ECTS 9
Work load
- Contact time
- Self study
Work load:
270 h
Class time:
90 h / 6 SWS
Self study:
180 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction English
Type of Exam

Written or oral exam

Content

This course covers sequence-based bioinformatics and evolution. The main topics
are pairwise alignment, BLAST and related heuristics, suffix trees and
their applications, sequence assembly, multiple alignment, hidden Markov models,
gene finding, motif finding, machine learning methods, models of DNA
evolution, phylogeny, whole genome phylogeny, computational methods in genomics,
and metagenomics.

Objectives

The first aim of this course is to introduce students to advanced concepts and
methods in bioinformatics, focusing on algorithmic, computational and mathematical
aspects. The second aim of this course is to enable students to apply
advanced methods to problems in molecular biology and related fields. After taking
this class, students will have a good understanding of the most important
approaches in sequence-based bioinformatics, will know which problems can be
addressed by the methods and will know how to apply such methods.

Prerequisite for participation There are no specific prerequisites.
Lecturer Huson
Literature / Other

Lecture notes and scientific publications

Last offered unknown
Planned for currently not planned
Assigned Study Areas BIO-SEQ, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-BIOINFO, MEDZ-RES, ML-CS



Module Number

INFO-4380
Module Title

Gaze-based Human-Computer Interaction
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction English
Type of Exam

Written exam (if the number of participants is small, an oral exam may be required)

Content

Topics: In-depth topics in visual perception (fixations, saccades, gaze patterns), mechanisms of visual attention, measurement of eye movements (eye-tracking), analysis of eye-tracking data with machine learning methods, gaze-based control of computer systems, use of gaze information for interactive systems (incl. applications in virtual (VR) and augmented reality (AR)).

Objectives

The students master theoretical methods of gaze-based interaction and can apply them in a problem-oriented manner. They are able to implement specialist knowledge and research methods acquired during their studies in a project and apply them to industrial practice. The students also master machine learning methods for the analysis and interpretation of eye-tracking data in the field of human-machine interaction and are able to transfer these independently to problem areas in computer science and apply them appropriately to the situation.

Prerequisite for participation There are no specific prerequisites.
Lecturer Kübler
Literature / Other

Vorlesungsfolien, zusätzliche Literatur wird bekanntgegeben

Last offered ---
Planned for ---
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-HCI, MEDI-INFO, MEDI-MEDI, ML-CS



Module Number

INFO-4381
Module Title

Advanced Topics in Human-Computer Interaction
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Oral presentation of at least 30 minutes and written report (essay at least 8 pages)

Content

This seminar covers current and varying topics from research and application
in the field of (multimodal) human-machine interaction.

Objectives

Students will read and reflect upon current research in the area of humancomputer
interaction. They can present current research results to other students
and researchers as well as lead research discussions. They can summarize
and evaluate the results of a paper in the form of a written research report.

Prerequisite for participation There are no specific prerequisites.
Lecturer Castner, Häufle
Literature / Other

-

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-HCI, MEDI-INFO, MEDI-MEDI, MEDZ-SEM, ML-CS



Module Number

INFO-4431
Module Title

Methods of Discrete Mathematics in Computer Science
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written Test

Content

inhalt 4431

Objectives

goals 4431

Prerequisite for participation There are no specific prerequisites.
Lecturer Kaufmann
Literature / Other

-

Last offered -
Planned for -
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4411
Module Title

Algorithm Engineering
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written Test

Content

inhalt 4411

Objectives

goals 4411

Prerequisite for participation There are no specific prerequisites.
Lecturer Kaufmann
Literature / Other

-

Last offered -
Planned for -
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4412
Module Title

Algorithms and Complexity
Lecture Type(s)

Lecture, Tutorial
ECTS 9
Work load
- Contact time
- Self study
Work load:
270 h
Class time:
90 h / 6 SWS
Self study:
180 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

Topics include matching, MinCostFlow, linear programming, approximation schemes, network analysis, algorithmic geometry, complexity issues such as lower bounds.

Objectives

Students deepen their knowledge of algorithmic techniques in various problem areas. This includes the application of complex graph algorithms, the mastery of strategies for network analysis as well as the ability to apply and develop approximation methods themselves. In the area of complexity issues, students are able to assess problems according to their degree of difficulty and also prove these assessments using the techniques they have learned.

Prerequisite for participation There are no specific prerequisites.
Lecturer Kaufmann
Literature / Other

Raghavan, Magnati, Orlin: Network Algorithms
Mehlhorn, Näher: LEDA - A platform for combinatorial and geometric computation
Papadimitriou, Steiglitz: Combinatorial optimization : algorithms and complexity

Last offered ---
Planned for ---
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4415
Module Title

Randomized Algorithms
Lecture Type(s)

Lecture
ECTS 9
Work load
- Contact time
- Self study
Work load:
270 h
Class time:
90 h / 6 SWS
Self study:
180 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written Test

Content

inhalt 4415

Objectives

goals 4415

Prerequisite for participation There are no specific prerequisites.
Lecturer Kaufmann
Literature / Other

-

Last offered -
Planned for -
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4419
Module Title

Advanced Topics in Algorithmics
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

The module includes in-depth courses in algorithms that complement the basic modules in this area. It is aimed primarily at students who wish to acquire knowledge specifically in this area.

Objectives

The students are able to classify special topics of algorithms and to analyse and evaluate related algorithms. They are able to transfer the concepts to new applications and design their own solution strategies. They develop their final thesis in these topics.

Prerequisite for participation There are no specific prerequisites.
Lecturer Kaufmann, Schlipf
Literature / Other

Raghavan, Magnati, Orlin: Network Algorithms
Mehlhorn, Näher: LEDA - A platform for combinatorial and geometric computation
Papadimitriou, Steiglitz: Combinatorial optimization : algorithms and complexity

Last offered unknown
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4520
Module Title

Network Algorithms
Lecture Type(s)

Practical Course
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction German and English
Type of Exam

Acceptance of the internship project in the course of the semester, both the presentation and the elaboration are included in the grade.

Content

This practical course deepens individual practical aspects of network algorithms as addressed in corresponding courses, e.g. Methods of Algorithmics, Algorithms and Complexity, or Discrete Optimization.

Objectives

The students can implement several of the methods in software technology. This implementation ranges from requirements analysis, design and implementation to text and documentation.

Prerequisite for participation There are no specific prerequisites.
Lecturer Kaufmann
Literature / Other

Originalliteratur wird bekanntgegeben

Last offered unknown
Planned for Wintersemester 2023
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-THEO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDI-WEB, ML-CS



Module Number

INFO-4653
Module Title

Combinatorial Algorithms
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

The final grade is determined by the presentation, elaboration and participation in the discussions.

Content

The seminar involves the development of written sources on topics in the areas of Efficient Algorithms under supervision. Presentation and the written summary conclude the seminar work in each case. Active participation in the individual sessions is an important part of the seminar.

Objectives

The students can independently work out and understand an extended and complex subject from the area of combinatorial algorithms from a written source and present it in the form of a lecture and also represent it in a discussion in front of a plenum. In addition to the oral presentation, they can present and summarise the elaborated topic in writing.

Prerequisite for participation There are no specific prerequisites.
Lecturer Kaufmann
Literature / Other

wechselnd

Last offered unknown
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS



Module Number

INFO-4432
Module Title

Discrete Optimization
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction German and English
Type of Exam

Written exam (oral exam with a small number of participants), exercise points
can be included as bonus points in the exam evaluation

Content

Topics include basics of linear optimization, methods of linear optimization, especially simplex algorithm, basics of integer optimization, branch-and-bound, cutting planes, and selected examples of of combinatorial optimization

Objectives

The students know some important algorithms of linear, integer and combinatorial optimisation as well as the underlying theoretical methods. They are able to assess the methods with regard to their complexity. By formally correctly writing down the solutions and implementing the methods presented in the lecture, the students acquire necessary competences for their own scientific work.

Prerequisite for participation There are no specific prerequisites.
Lecturer Kaufmann
Literature / Other

Nemhauser, Wolsey: Integer and Combinatorial Optimization, Wiley (1999)
Skript zur Vorlesung

Last offered ---
Planned for ---
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

BIOINF4120 (enstpricht BIO-4120)
Module Title

Bioinformatics of Structures and Systems
Lecture Type(s)

Lecture, Tutorial
ECTS 9
Work load
- Contact time
- Self study
Work load:
270 h
Class time:
90 h / 6 SWS
Self study:
180 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Oral exam or, in case of too many students, written exam. 50% of the achievable points from the assignments and the project, individually, are required for exam admission. Points achieved in excess of 50% serve as a bonus for the final exam.

Content

The lecture will focus on RNA structure and structure prediction, protein structures and their modeling, protein structure prediction, methods and concepts of systems biology, algorithms for the analysis of expression data and biological networks (concepts, inference, simulation). The lecture goes into more depth on the topics already included in the BSc module 'Fundamentals of Bioinformatics', covering in particular advanced techniques and research-related applications. Project work on research-related topics is embedded in the lecture.

Objectives

Students can abstract and formalise structural and systems biology problems. They know competent applications of common procedures and tools of structural and systems bioinformatics and can apply them to biological data. They have strengthened their language competence (English) in listening comprehension, writing and presentation.

Prerequisite for participation There are no specific prerequisites.
Lecturer Kohlbacher
Literature / Other

Folien zur Vorlesung
Branden, Tooze: Introduction to Protein Structure, Garland Science, 1998
Andrew Leach: Molecular Modeling. Principles and Applications,
Prentice Hall, 2nd ed., 2001

Last offered Sommersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas BIO-STRUK, INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDZ-BIOINFO, MEDZ-RES, ML-CS



Module Number

BIOINF4220 (entspricht BIO-4220)
Module Title

Integrative Bioinformatics
Lecture Type(s)

Practical Course
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

A written report is to be submitted after the course. Performance during the course will also be integrated into the final grade.

Content

The basics of modelling biological data and integration of heterogeneous datasets
are conveyed and applied on concrete examples in this practical course.
Using the scripting language Python the data is parsed and consolidated in a
database. Biologically relevant, demonstrative analyses are performed on this
integrated data. Data integration, exploration, visualization, statistical tests
and machine learning are applied on a dataset of genomic, transcriptomic, metabolomic
and phenomic data from genetically equal samples.

Objectives

(1)The students learn how to parse and integrate heterogeneous biological data
into databases. (2) They learn how to perform statistical analyses on biological
data and to summarize and illustrate their results visually. (3) They learn how
to interpret the results of integrative analyses and report on these results in a
concise manner.

Prerequisite for participation BIOINF4120 (enstpricht BIO-4120) Bioinformatics of Structures and Systems
Lecturer Kohlbacher
Literature / Other

Will be supplied during the course.

Last offered Sommersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas BIO-PRAK



Module Number

BIOINF4230 (entspricht BIO-4230)
Module Title

Applied Structure-Based Drug Design
Lecture Type(s)

Practical Course
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written elaboration and presentation based on it

Content

This module deals with the practical application of basic techniques and tools of computer-aided drug design. The program packages BALLView, VMD, Glide, Prime and Modeller are used for this purpose. Initially, the focus is on the preparation and visualization of 3D structures. In addition, specific intramolecular interactions of protein-ligand complexes are examined in more detail and selected ligands are docked into a pharmaceutically interesting target. The second part of the lab focuses on virtual high-throughput screening and rational structure-based drug design.

Objectives

(1) Students are fundamentally able to handle standard tools of structure-based drug design, (2) are familiar with the practical handling of protein and ligand structures and (3) are able to interpret the results of these tools and techniques and critically assess them with regard to their relevance.

Prerequisite for participation There are no specific prerequisites.
Lecturer Kohlbacher
Literature / Other

Materialien werden zur Verfügung gestellt.

Last offered unknown
Planned for currently not planned
Assigned Study Areas BIO-PRAK, MEDZ-BIOMED, MEDZ-RES



Module Number

BIOINF4352 (entspricht BIO-4352)
Module Title

Computational Proteomics and Metabolomics
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction English
Type of Exam

Oral exam or, in case of too many students, written exam. 50% of the achievable points from the assignments and the project, individually, are required for exam admission. Points achieved in excess of 50% serve as a bonus for the final exam.

Content

Communicate the current state of the art in bioinformatics applications in proteomics and metabolomics with an emphasis on the analysis of mass spectrometry data. Topics include biological issues, experimental techniques, database searching, de novo sequencing, protein inference, quantification of peptides and metabolites, identification of metabolites.

Objectives

The students know the current research standards in the field of proteomics and metabolomics and can transfer known bioinformatics techniques to problems in these fields.

Prerequisite for participation There are no specific prerequisites.
Lecturer Kohlbacher
Literature / Other

Originalarbeiten und zusätzliche Materialien werden ausgegeben.

Last offered unknown
Planned for Sommersemester 2024
Assigned Study Areas BIO-BIO, INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, MEDZ-RES, ML-CS



Module Number

BIONF4371 (bisher BIO-4371)
Module Title

Structure-Based Drug Design
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Oral exam or, in case of too many students, written exam. 50% of the achievable points from the assignments and the project, individually, are required for exam admission. Points achieved in excess of 50% serve as a bonus for the final exam.

Content

Starting with a broad introduction of the pharmaceutical drug development
process, the lecture conveys key concepts of structure-based computer-aided
drug design (CADD). Required basics on pharmaceutical key concepts are discussed
followed by basic concepts for modeling of 3D structures (ligands and
proteins). In the second part key physicochemical interactions between proteins
and ligands are presented, forming the basis to discuss strategies to predict
protein-ligand binding with a strong focus on algorithms for protein-ligand
docking. Finally, the challenging task of estimating binding affinities between
proteins and ligands in silico is introduced, leading to the discussion of scoring
functions, which are developed und used for that purpose.

Objectives

Students have a working knowledge on the pharmaceutical development process.
They are familiar with protein and ligand structures, with standard methods
to resolve them experimentally, with methods to model 3D structures,
and are able to identify relevant physicochemical interactions between them.
They have detailed knowledge of algorithmic techniques to predict proteinligand
binding (docking and scoring). The students are able to implement methods
to work with protein-ligand structures and to develop simple CADD
tools. Project work strengthened their ability to work in a team and to write
down and to present scientific work.

Prerequisite for participation There are no specific prerequisites.
Lecturer Kohlbacher
Literature / Other

Lecture slides and additional materials will be provided digitally.

Basic knowledge of protein structure, organic chemistry, and programming skills in Python are recommended.

Recommended textbooks:
1) Leach A. Molecular Modelling", Prentice Hall 2001
2) Schlick T. Molecular Modeling and Simulation", Springer 2010
3) Klebe G. "Wirkstoffdesign", Springer 2009

Last offered Sommersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas BIO-BIO, INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, MEDZ-RES, ML-CS



Module Number

BIOINF4372 (entspricht BIO-4372)
Module Title

Cheminformatics
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Oral exam or, in case of too many students, written exam. 50% of the achievable points from the assignments and the project, individually, are required for exam admission. Points achieved in excess of 50% serve as a bonus for the final exam.

Content

Starting with an overview of its main application area, namely drug design, the
lecture teaches how computer science methods can be used to work with chemical
data, strongly focusing on small organic molecules (compounds). Representation
of compounds (graphs, line notations, file formats) is followed by most
important ways of topological comparison (identity, substructure, similarity).
Relevant applications of topological similarity are introduced (searching, clustering,
library generation). Quantitative Structure-Activity Relationship (QSAR)
is introduced as the cheminformatics branch for predictive modeling of chemical
properties. Finally, the prediction of 3D-structures from topology and similarity
methods for compounds with 3D coordinates are introduced.

Objectives

Students know how different kinds of chemical data can be handled with computers,
how to represent and to analyse that data with methods from computer
science, and they have an overview of the main application area drug design.
Having understood the fundamental SSimilar Property Principlethey are able
to handle and to analyse experimental screening data and to implement
and apply ligand-based screening methods. Students have a solid knowledge on
standard tools and software libraries for cheminformatics. Project work strengthened
their ability to work in a team and to write down and to present scientific
work.

Prerequisite for participation There are no specific prerequisites.
Lecturer Kohlbacher
Literature / Other

Lecture slides and additional materials will be provided digitally. Basic knowledge of organic chemistry, graph theory, and programming skills in Python are recommended.

Recommended textbooks:
1) Leach A., Gillet V. An Introduction To Chemoinformatics", Springer 2007
2) Faulon J.-L., Bender A. (Eds.) "Handbook Of Chemoinformatics Algorithms", CRC Press 2010
3) Engel T., Gasteiger J. (Eds.) Chemoinformatics", Wiley-VCH 2018
4) Optional: Klebe G. "Wirkstoffdesign", Springer 2009

Last offered Wintersemester 2022
Planned for currently not planned
Assigned Study Areas BIO-BIO, INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, MEDZ-RES, ML-CS



Module Number

BIOINF4381 (entspricht BIO-4381)
Module Title

Systems Immunology
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written elaboration and presentation based on it

Content

Systems immunology links the methods of modern systems biology with applications in immunology. In this current field of research, in addition to high-throughput immunology data, mathematical modeling techniques are used to provide new insights into the dynamics of the immune system. In this module, work from the methodological foundations (systems biology) and current research on the application of these methods in immunology will be developed, thus providing an overview of this very current field of research.

Objectives

Students have an overview of the field of systems immunobiology. They can apply known bioinformatics techniques to problems in immunology. They have deepened their English language and presentation skills.

Prerequisite for participation There are no specific prerequisites.
Lecturer Kohlbacher
Literature / Other

Originalarbeiten und zusätzliche Materialien werden im Seminar ausgegeben.

Last offered unknown
Planned for currently not planned
Assigned Study Areas BIO-BIO, BIO-SEM, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-SEM, ML-CS



Module Number

BIOINF4364 (entspricht BIO-4364)
Module Title

Visualization of Biological Data
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Oral examination (written exam if there are a large number of participants)

Content

As biological datasets increase in size and complexity, we are moving more
and more from an hypothesis-driven research paradigm to a data-driven one.
As a result, the visual exploration of that data has become even more crucial
than in the past. The aim of this lecture is to familiarize the participants
with modern methodologies of Information Visualization and Visual Analytics.
Information Visualization is concerned with methods for the visualization of
abstract data that has no inherent spatial structure (the visualization of spatial
data is covered in INF3145 - Scientific Visualization). The lecture imparts
how to apply these methods to biological data using practical examples and
provides hands-on training during the tutorials. Questions such as ‘what is data
visualization’, ‘what is visual analytics’, and ‘how can we visualise (biological)
data to gain insight in them, so that hypotheses can be generated or explored
and further targeted analyses can be defined’ are discussed. No prior knowledge
of biology is required, that is, the lecture is also suitable for students from other
fields such as computer science or media/medical informatics.

Objectives

Students understand the visual analysis process. They know basic methods of
information visualization and the ‘do’s’ and ‘don’ts’ of visualization. The know
methods to visualize diverse biological data like genomics or transcriptomics
data. They are able to chose suitable visualizations based on the type of data
and the given analysis task. The students will be able to design and develop
complex, interactive visual analytics applications in small teams.

Prerequisite for participation There are no specific prerequisites.
Lecturer Krone
Literature / Other

Lecture slides will be provided for download. Tamara Munzner ‘Visualization
Analysis and Design’, A K Peters, 2014. Nature Methods Supplement ‘Visualizing
biological data’, various Nature Methods ‘Points of View’ articles.

Last offered unknown
Planned for currently not planned
Assigned Study Areas BIO-BIO, INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-BIOMED, ML-CS



Module Number

INFO-4222
Module Title

Software Quality in Theory and Industrial Practice
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction German
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

Most industrial systems are no longer conceivable without software. Particularly in safety-relevant areas, such as automotive or aircraft construction, software systems are increasingly being used. Classical hardware-dominated systems are gradually being replaced by software-dominated systems. The design of these software systems represents an ever increasing challenge for system developers. Growing time pressure, higher demands on correctness and increasing system complexity make software development a complex task. This inevitably increases the potential for errors. For this reason, the testing of software systems is becoming more and more important. In this lecture the basics for testing, debugging and verifying software systems are described. Main topics are among others: Quality management, function-oriented testing, coverage analysis techniques (coverage methods), input space partitioning, special testing techniques, software measurement, debugging, formal techniques, testing strategies, and testing embedded software. The lecture covers not only the theoretical basics of the listed topics, but also emphasizes the industrial practical relevance. All areas covered can be directly applied in the industrial software environment. Furthermore, the two lecturers Dr. Jürgen Ruf (Bosch Sensortec) and Prof. Dr. Thomas Kropf (Bosch), who both come from industry, bring a lot of practical experience with them and want to convey this to the students in the lecture. The lecture is based, among other things, on current research topics of the "Safety Critical Systems Group" of the Computer Engineering Department.

Objectives

The students know basic principles and working techniques for ensuring high software quality and can critically question them. In addition to testing and verification, this also includes process models for software development. They are able to use analysis and test methods to increase software quality.

Prerequisite for participation There are no specific prerequisites.
Lecturer Bringmann, Kropf
Literature / Other

• Liggesmeyer, P.: Software-Qualität: Testen, Analysieren und Verifizieren
von Software
• Ammann, P; Offutt, J.: Introduction to Software Testing

Last offered Sommersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS



Module Number

INFO-4353
Module Title

Selected Topics in Computer Security
Lecture Type(s)

Lecture
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction German
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

The lecture covers the following topics. Authentication and Authorization: Theory, Hardware Concepts and UNIX Concepts, Cryptography: Encryption, MACs, Signatures and Key Management, the PKCS #11 Standard and Hardware Security Modules (HSMs), Security in the Cloud, Confidential Computing, Quantum Computing.

Objectives

Students have advanced knowledge of computer security with regard to hardware and software. They have practice-relevant special knowledge (basic principles according to which a computer system can be secured, security standards, cryptographic API) and can also apply this to problem solving in new and unfamiliar contexts. They are able to independently acquire new knowledge and skills and to exchange information, ideas, problems and solutions with experts at a scientific level.

Prerequisite for participation There are no specific prerequisites.
Lecturer Bündgen, Menth
Literature / Other

• C. Eckert: IT-Sicherheit, Oldenbourg Wissenschaftsverlag
• N. Ferguson, B. Schneier, T. Kohno: Cryptography Engineering, Wiley
2010

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4421
Module Title

Computability
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German
Type of Exam

Term Paper

Content

The course gives an introduction to computability theory, covering various computability models such as partial recursive functions and Turing machines, the halting problem, and Rice's theorem.

Objectives

Students acquire knowledge of the formal limits of computability

Prerequisite for participation There are no specific prerequisites.
Lecturer
Literature / Other

-

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4422
Module Title

Circuit Complexity
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written Test

Content

inhalt 4422

Objectives

goals 4422

Prerequisite for participation There are no specific prerequisites.
Lecturer
Literature / Other

-

Last offered -
Planned for -
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4441
Module Title

Petri Nets
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written Test

Content

inhalt 4441

Objectives

goals 4441

Prerequisite for participation There are no specific prerequisites.
Lecturer
Literature / Other

-

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4442
Module Title

Model Checking
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written Test

Content

inhalt 4442

Objectives

goals 4442

Prerequisite for participation There are no specific prerequisites.
Lecturer
Literature / Other

-

Last offered -
Planned for -
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4443
Module Title

Formal Languages II
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written Test

Content

inhalt 4443

Objectives

goals 4443

Prerequisite for participation There are no specific prerequisites.
Lecturer
Literature / Other

-

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4444
Module Title

Complexity Theory II
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German
Type of Exam

Oral examination (written exam if there are a large number of participants)

Content

Building on the lecture Complexity Theory, the in-depth topics include (non) uniform circuit classes, approximation theory, and randomization. In addition, barriers in the form of relativization and natural proofs are considered.

Objectives

Students have an overview of different complexity classes, circuits and randomisation and are able to write a master thesis in this field.

Prerequisite for participation There are no specific prerequisites.
Lecturer
Literature / Other

-

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4497
Module Title

Special Chapters in Theoretical Computer Science I
Lecture Type(s)

Lecture
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written Test

Content

inhalt 4497

Objectives

goals 4497

Prerequisite for participation There are no specific prerequisites.
Lecturer
Literature / Other

-

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4498
Module Title

Special Chapters in Theoretical Computer Science II
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written Test

Content

inhalt 4498

Objectives

goals 4498

Prerequisite for participation There are no specific prerequisites.
Lecturer
Literature / Other

-

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4499
Module Title

Special Chapters in Theoretical Computer Science III
Lecture Type(s)

Lecture
ECTS 9
Work load
- Contact time
- Self study
Work load:
270 h
Class time:
90 h / 6 SWS
Self study:
180 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written Test

Content

inhalt 4499

Objectives

goals 4499

Prerequisite for participation There are no specific prerequisites.
Lecturer
Literature / Other

-

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4656
Module Title

Seminar Theoretical Computer Science
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

course-dependent

Content

Varying topics in the field of theoretical computer science; depending on the specific seminar topic (see seminar offers in alma).

Note on the summer term 2024:
In this term the following seminars will be offered in this module:
- "Algorithmische Geometie: Aktuelle Themen" (Schlipf)
- "Ethical Hacking (Huber)

Objectives

In-depth knowledge of approaches and methods in theoretical computer science

Prerequisite for participation There are no specific prerequisites.
Lecturer Schlipf, wechselnde Dozenten
Literature / Other

je nach Seminarthema

Last offered Wintersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS



Module Number

INFO-4167
Module Title

Computer Graphics
Lecture Type(s)

Practical Course
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction English
Type of Exam

Project, presentation and elaboration

Content

Implementation of advanced applications and programs in the field of computer graphics / computer vision

Objectives

Students know how current approaches in the areas of rendering, GPU-based programming, displays or computational photography can be efficiently implemented with appropriate hardware. They can independently plan and implement programming projects in groups using programming languages developed for GPUs, input and output hardware and suitable libraries.

Prerequisite for participation There are no specific prerequisites.
Lecturer Lensch
Literature / Other

Entwicklungsumgebung wird zur Verfügung gestellt

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-PRAX, MEDI-VIS, ML-CS



Module Number

INFO-4173
Module Title

Massively Parallel Computing
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction English
Type of Exam

Oral examination (written exam if there are a large number of participants), successful exercises can result in a grade bonus

Content

The lecture introduces the necessary concepts of parallel processing, and gives an overview of the currently available hardware. Furthermore, basic parallel algorithms, e.g. Map, Reduce, Prefix Sum, Branching, but also parallel applications like FFT, particle systems and simulations etc. are covered. In order to develop efficient parallel solutions for new problems, appropriate approaches and complexity analyses will be taught.

Objectives

A current trend of all chip manufacturers is to integrate more and more computing units on one chip, e.g. with several hundred processors on one graphics card. In order to use these architectures efficiently, suitable algorithms must be chosen and the problems optimised in terms of memory bandwidth. (1) The aim of the lecture is to enable the students to analyse a given problem with regard to the possible increase in efficiency through parallelisation. (2) They are able to develop suitable algorithms to work out a massively parallel implementation as fast as possible. (3) They are able to optimise their programs in terms of memory bandwidth, GPU utilisation and registers by profiling.

Prerequisite for participation There are no specific prerequisites.
Lecturer Lensch
Literature / Other

Hubert Nguyen: GPU Gems 3, Addison Wesley; T. Mattson, B. Sanders, B.
Massingill: Patterns for Parallel Programming, Addison Wesley ; gpgpu.org
- General-Purpose Computation Using Graphics Hardware; NVIDIA CUDA
page; NVIDIA CUDA Programming Guide ; Vorlesungsfolien werden bereitgestellt

Last offered Sommersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDI-PRAX, MEDI-VIS, MEDZ-MEDTECH, ML-CS



Module Number

INFO-4174
Module Title

Massively Parallel Computing
Lecture Type(s)

Practical Course
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction English
Type of Exam

Presentation and written report

Content

The efficient implementation and realization of algorithms from different areas of computer science or related disciplines on massively parallel architectures will be taught. Furthermore, the programming of massively parallel computer systems GPU, and the associated challenges such as memory management, branching, synchronization are covered. In addition to GPU programming, the focus is also on measuring and comparing the performance of parallel applications.

Objectives

Students can independently (in small groups) plan, implement and execute the implementation of computationally intensive tasks on massively parallel computers. They are able to measure and analyse the runtime of parallel applications.

Prerequisite for participation INFO-4173 Massively Parallel Computing
Lecturer Lensch
Literature / Other

Entwicklungsumgebung wird zur Verfügung gestellt, NVIDIA CUDA page

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDI-PRAX, MEDI-VIS, MEDZ-MEDTECH, ML-CS



Module Number

INFO-4175
Module Title

Rendering
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction English
Type of Exam

Oral examination (written exam if there are a large number of participants), successful exercises can result in a grade bonus

Content

In-depth instruction in computer graphics with a focus on image synthesis is taught and practiced. The module covers basic and advanced theory and algorithms of Monte Carlo and quasi-MonteCarlo simulation, covers global illumination approaches such as path tracing, bidirectional path tracing, Metropolis sampling, photon mapping, as well as methods for real-time rendering.

Objectives

Students acquire in-depth knowledge in the areas of Monte Carlo approximation and global illumination simulation. Students will be able to analyse and implement the techniques covered as well as independently evaluate and solve problems and apply them in their own projects.

Prerequisite for participation There are no specific prerequisites.
Lecturer Lensch
Literature / Other

Pharr, Humphreys: Physically Based Rendering, Morgan Kaufmann, 2004 Dutré et al.: Advanced G / Grundkenntnisse im Bereich Computergrafik

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-HCI, MEDI-INFO, MEDI-MEDI, MEDI-VIS, ML-CS



Module Number

INFO-4176
Module Title

Computational Photography
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction English
Type of Exam

Oral examination (written exam if there are a large number of participants), successful exercises can result in a grade bonus

Content

In-depth instruction in digital photography, hardware, and subsequent image reconstruction and processing will be taught and practiced. The course covers basic and advanced theory and algorithms of image reconstruction, denoising, deconvolution, 3D image acquisition, computed tomography or compressive sensing. At the same time, different acquisition systems, camera sensors, active illumination and multi-camera systems will be covered, as well as the new image acquisition modalities that they enable.

Objectives

Students acquire in-depth knowledge in the areas of digital photography, computational imaging, and image-based processes. Students will be able to analyse the techniques covered and compare alternative approaches. In projects, you will be able to independently evaluate the problem and develop proposals for solutions. Students are able to realise solutions through implementations in software with the appropriate hardware.

Prerequisite for participation There are no specific prerequisites.
Lecturer Lensch
Literature / Other

Vorlesungsfolien werden bereitgestellt / Grundkenntnisse im Bereich Computergrafik

Last offered Sommersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-MEDTECH, ML-CS



Module Number

INFO-4178
Module Title

Displays
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction English
Type of Exam

Oral examination (written exam if there are a large number of participants), successful exercises can result in a grade bonus

Content

In-depth teaching content in the area of displays is taught and practiced. The module covers the structure and technology of monitors, projectors, AR/VR glasses, stereo displays, light field displays and other frahling methods. At the same time, the focus is on the algorithmic preparation of data for all types of displays.

Objectives

Students acquire in-depth knowledge of current display technologies. The students will be able to analyse the technologies discussed and evaluate alternatives. They can independently evaluate problems and develop their own solutions and implementations in projects.

Prerequisite for participation There are no specific prerequisites.
Lecturer Lensch
Literature / Other

Aktuelle Veröffentlichungen im Bereich Displays, Vorlesungsfolien werden bereitgestellt / Grundkenntnisse im Bereich Computergrafik

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-HCI, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDI-VIS, ML-CS



Module Number

INFO-4341
Module Title

Network Security I
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

Written exam (oral exam if the number of participants is small), exercises can be Bonus points will be considered into the exam.

Content

The lecture covers the following topics: Principles of Network Security, Cryptographic Methods, Public Key Infrastructure, Authentication, Application Layer Security, Transport Layer Security, Virtual Private Networks, Layer-2 Security, Perimeter Security, Anonymization, Blockchain, Advanced Topics; the lecture is accompanied by an extensive tutorial, which illustrates and deepens the acquired knowledge with practical examples.

Objectives

Students have a comprehensive and in-depth understanding of network security. They are able to apply their acquired problem-solving skills in new and unfamiliar contexts. They are able to acquire new knowledge and skills independently and to exchange information, ideas, problems and solutions with experts on a scientific level.

Prerequisite for participation INF3331 Computer Networking and Internet
Lecturer Menth
Literature / Other

-

Last offered Wintersemester 2021
Planned for Sommersemester 2023
Assigned Study Areas INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-WEB, ML-CS



Module Number

INFO-4342
Module Title

Network Security II (3 ECTS)
Lecture Type(s)

Lecture, Tutorial
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

Written exam (oral exam if the number of participants is small), points gained in the exercises may be transferable as bonus points into the exam.

Content

The lecture covers the following topics: Layer-2 Security, Perimeter Security, Anonymization, Blockchain, Advanced Topics; the lecture is accompanied by an extensive practice session that illustrates and deepens the acquired knowledge with practical examples.

Objectives

Network Security II: Students have a comprehensive and in-depth understanding of network security. They are able to apply their acquired problem-solving skills also in new and unfamiliar contexts. They are able to acquire new knowledge and skills independently and to exchange information, ideas, problems and solutions with experts on a scientific level.

Prerequisite for participation INFO-4341 Network Security I
Lecturer Menth
Literature / Other

-

Last offered Wintersemester 2021
Planned for Sommersemester 2023
Assigned Study Areas INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-WEB, ML-CS



Module Number

INFO-4343
Module Title

Network Security III (Lab)
Lecture Type(s)

Practical Course
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

Graded internship attempts consisting of theory and practice with a final exam or oral exam

Content

Introductory lecture sessions for each experiment, hands-on exercises at home to become familiar with the experimental environment (Linux command line, basic network administration commands, traffic recording), and graded classroom exercises in the experimental lab on the following topics: Network security, attacks and attack defense, VPN, advanced routing methods, wifi, selected application protocols.

Objectives

The students deepen their practical knowledge of communication networks considerably, especially with regard to practical, security-relevant aspects. They can independently acquire new knowledge and skills and apply their problem-solving abilities in new and unfamiliar contexts. They learn to communicate their knowledge in a clear and unambiguous manner and to exchange it at a scientific level.

Prerequisite for participation INF3331 Computer Networking and Internet,

INFO-4341 Network Security I,

INFO-4342 Network Security II (3 ECTS)
Lecturer Menth
Literature / Other

-

Last offered Wintersemester 2021
Planned for Sommersemester 2023
Assigned Study Areas INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4344
Module Title

Communication Networks Lab
Lecture Type(s)

Practical Course
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

Graded internship attempts and an oral or written final exam

Content

Introductory lecture sessions for each experiment, hands-on exercises at home to become familiar with the experimental environment (Linux command line, basic network administration commands, traffic recording), and graded classroom exercises in the experimental lab on advanced topics. Alternate Topics on current communication technologies.

Objectives

Students deepen their practical knowledge of communication networks significantly, especially on the topics taught. They can independently carry out very demanding configurations of computer networks and experimentally evaluate properties of advanced protocols. They can independently acquire new knowledge and skills and apply their problem-solving abilities in new and unfamiliar contexts. They learn to communicate and exchange their knowledge in a clear and unambiguous manner at a scientific level.

Prerequisite for participation INF3331 Computer Networking and Internet
Lecturer Menth
Literature / Other

-

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4345
Module Title

Modeling and Simulation I
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

Exam, exercise performance can flow into the exam as bonus points

Content

Statistical basics (part 1), introduction to simulation techniques, distribution functions (part 1), sample preparation, statistical basics (part 2), statistical evaluation of simulations, stochastic processes, discrete-time Markov chains, continuous-time Markov chains; the lecture contents are put into practice during the exercise, in particular a simple simulator is built in several consecutive exercises. The focus is on the modeling of problems from different contexts by means of suitable distribution functions as well as by Markov chains, and on the associated mathematical foundations.

Objectives

The students can model and examine technical systems in their conception phase with sophisticated methods and thus efficiently participate in research and development. They have in-depth, practical knowledge of discrete-time simulation and can systematically set up and evaluate experiments. They can use discrete-time and continuous-time Markov chains for the modelling and investigation of technical systems and predict their performance with the help of queueing theory. They are able to transfer and apply the acquired knowledge in new contexts.

Prerequisite for participation There are no specific prerequisites.
Lecturer Menth
Literature / Other

-

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4346
Module Title

Modeling and Simulation II
Lecture Type(s)

Lecture
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

Exam, exercise performance can flow into the exam as bonus points

Content

Distribution functions (part 2), multidimensional random structures, design of experiments, significance tests of hypotheses and their applications, investigation of measured data, time-dependent statistics, possibility and limits of model building and simulation, random number generation; the lecture conveys the theoretical basics and in the exercise the lecture contents are implemented by programming tasks based on practical examples.

Objectives

Students will be able to model and examine technical systems in their conception phase using sophisticated methods and thus efficiently contribute to research and development. They are able to transfer and apply the acquired knowledge in new contexts. They can critically question learned methods and thus decide whether they are suitable for given problems. Through the acquired mathematical understanding, they can modify learned tools for new problems in a suitable manner.

Prerequisite for participation INFO-4345 Modeling and Simulation I
Lecturer Menth
Literature / Other

-

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4347
Module Title

Network Softwarization
Lecture Type(s)

Lecture, Tutorial
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction German
Type of Exam

Written exam (oral exam if the number of participants is small), points gained in the exercises may be transferable as bonus points into the exam.

Content

The lecture gives an overview of relevant technologies in the area of Network Softwarization. This includes Software Defined Networking using Open Flow, Data Plane Programming using P4 as well as Virtualization techniques and Network Function Virtualization. Additionally, current research topics will be discussed. The course is accompanied by practical exercises in which the students apply the learned technologies and solve challenging programming tasks.

Objectives

Students have acquired state-of-the-art knowledge and skills in network softwarization and insight into research topics, which makes them particularly qualified for research work in the field of communication networks. They have an in-depth understanding of how network software can be used to develop new technologies faster and operate networks more efficiently. They can acquire new knowledge and skills independently and carry out application-oriented projects in a largely self-directed manner. They are able to exchange information, ideas, problems and solutions with experts and laypersons on a scientific level.

Prerequisite for participation INF3331 Computer Networking and Internet,

INFO-4341 Network Security I,

INFO-4342 Network Security II (3 ECTS)
Lecturer Menth
Literature / Other

-

Last offered Sommersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-WEB, ML-CS



Module Number

INFO-4348
Module Title

Communication Technologies 1
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German
Type of Exam

Written exam (oral exam if the number of participants is small), points achieved in the exercises may be considered as bonus points in the exam.

Content

The lecture provides knowledge on advanced topics in the field of communication networks, in contrast to INF3331 "Grundlagen des Internets" this lecture deals mainly with the communication layers below the IPLayer.

Topics are: Data and signals, conversion of data into digital signals, modulation of digital and analog signals, multiplex techniques, transmission media, switching, data link control, multiple access protocols, Ethernet, backbone concepts, VLANs, software-defined networking, quality of service, time-sensitive networking (TSN), bus systems in vehicles.

Objectives

Communication Technologies 1: Students have a comprehensive and deep understanding of the operating principle and organisation of communication networks. They are able to exchange information, ideas, problems and solutions with experts and non-experts at a scientific level. They can independently acquire new knowledge and skills. They have acquired a critical understanding at the cutting edge of knowledge in several specialised areas and can apply their problem-solving skills in new and unfamiliar situations.

Prerequisite for participation INF3331 Computer Networking and Internet
Lecturer Menth
Literature / Other

-

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4349
Module Title

Communication Technologies 2
Lecture Type(s)

Lecture, Tutorial
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction German
Type of Exam

Written exam (oral exam if the number of participants is small), exercises can be
Bonus points will be considered into the exam.

Content

The lecture provides knowledge on advanced topics in the field of communication networks, in contrast to INF3331 "Grundlagen des Internets" this lecture deals mainly with the communication layers below the IPLayer.
Topics are: Optical communication networks, ATM, Frame Relay, MPLS, wifi, Bluetooth, LoRaWAN, mobile radio, telephone, DSL, cable, Voice-over-IP.

Objectives

Students have a comprehensive and deep understanding of the operating principle and organisation of communication networks. They are able to exchange information, ideas, problems and solutions with experts and non-experts at a scientific level. They are able to acquire new knowledge and skills independently. They have acquired a critical understanding at the cutting edge of knowledge in several specialised areas and can apply their problem-solving skills in new and unfamiliar situations.

Prerequisite for participation INFO-4348 Communication Technologies 1
Lecturer Menth
Literature / Other

-

Last offered -
Planned for -
Assigned Study Areas INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-WEB, ML-CS



Module Number

INFO-4350
Module Title

Selected Topics in Communication Networks
Lecture Type(s)

Lecture
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction German
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

Changing in-depth topics from the subfields of the research field of communication networks.

Objectives

The students have in-depth knowledge in the field of communication networks. They can apply their acquired problem-solving skills in new and unfamiliar contexts. They are able to independently acquire new knowledge and skills in the subject area and carry out independent research or application-oriented projects. They are able to exchange information, ideas, problems and solutions with experts and non-experts at a scientific level.

Prerequisite for participation There are no specific prerequisites.
Lecturer Menth
Literature / Other

-

Last offered -
Planned for -
Assigned Study Areas INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4354
Module Title

Public Cloud Computing
Lecture Type(s)

Lecture, Tutorial
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction German
Type of Exam

Written exam (oral exam if the number of participants is small), points received in exercises may be transferable as bonus points into the exam.

Content

The lecture provides advanced knowledge in container technology, Software-as-a-Service (SaaS) and Function-as-a-Service (FaaS). Theoretical approaches to cloud architectures, cloud computing patterns and automation are discussed. Classic use cases such as serverless computing, Internet of Things (IoT), edge computing, business intelligence (BI) and machine learning (ML) will be demonstrated. The tutorial consists of extensive, hands-on assignments and specifically addresses operations, economic efficiency, and security.

Objectives

The students have advanced, current knowledge in the area of public cloud computing. They can independently acquire new knowledge and skills and carry out largely self-directed application-oriented projects. They are able to exchange information, ideas, problems and solutions with experts and laypersons on a scientific level and to assume responsibility in a team.

Prerequisite for participation INF3331 Computer Networking and Internet,

INFO-4341 Network Security I
Lecturer Menth
Literature / Other

-

Last offered unknown
Planned for Wintersemester 2023
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4352
Module Title

Pentesting
Lecture Type(s)

Lecture
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction German
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

To secure networks or applications, an administrator or developer needs knowledge about existing vulnerabilities. These can be efficiently uncovered through simulated hacker attacks, so-called penetration tests. In addition to theoretical basics about the planning and execution of penetration tests, this lecture provides in-depth practical knowledge of modern attack tools, current vulnerabilities and the methodology to exploit them. The spectrum ranges from footprinting to the actual attack to the placement of backdoors in a compromised system. Lecture and exercises will take place as a closely interlinked block course. Topics are: Penetration testing design options, testing modules, estimating testing effort, assessing results and documentation, tracking vulnerabilities, ethical and legal issues, penetration testing standards, footprinting, portscanning, enumeration, sniffing, attacks against encryption, common configurational vulnerabilities, methodical security analysis of web applications and typical web application vulnerabilities, dealing with metasploit, attacks against Windows networks, privilege escalation, backdoors, online and offline attacks against passwords, vulnerability analysis, exploitation of buffer overflow vulnerabilities.

Objectives

Students significantly deepen their understanding of IT systems and are enabled to recognise and remedy security vulnerabilities. They have the ability to apply the knowledge in new, unfamiliar contexts and to acquire new knowledge independently. In addition, they learn to adequately include ethical aspects.

Prerequisite for participation There are no specific prerequisites.
Lecturer Menth
Literature / Other

-

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4351
Module Title

Communication Networks (Seminar)
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Oral presentation and written report

Content

Dieses Seminar behandelt aktuelle und wechselnde Themen aus Forschung und Anwendung auf dem Gebiet der Kommunikationsnetze.

Objectives

Students are able to read, reflect, and examine the topic in substance upon
current research papers in the area of communication networks. They can critically
assess the contributions of a paper. They can present current research
results to other students and researchers, and can lead research discussions.
They can summarize and evaluate the results of research papers in form of a
oral presentation and a written report.

Prerequisite for participation INFO-4348 Communication Technologies 1
Lecturer Menth
Literature / Other

-

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-WEB, MEDZ-SEM, ML-CS



Module Number

BIOINF4376 (entspricht BIO-4376)
Module Title

Biomedical Data Management
Lecture Type(s)

Lecture, Seminar
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction English
Type of Exam

Oral exam or written exam with a large number of participants, successful participation in the seminar is a prerequisite for the exam

Content

Topics include the various technologies of quantitative biology/biomedicine (e.g. omics technologies, high-throughput screening and imaging), methods for processing and analyzing high-throughput data (bioinformatics workflows) and standardization of data exchange formats. Furthermore, the module deals with data and metadata models, as well as data storage (different approaches) and general concepts for data management (e.g. different database systems) and (web-based) visualization. The lecture thereby prepares the technical basics of the topics and in the seminar current work on biomedical application of these technologies is presented and discussed by the students.

Objectives

Students will master simple and multivariate statistical methods, as well as data-driven approaches for the management and analysis of high-throughput biomedical and imaging data. They are able to design research infrastructures and to use evaluation routines on different already existing infrastructures in a methodologically adequate way, as well as to critically question their use in publications. Furthermore, the students are able to design, conduct and adequately evaluate a qualitative/quantitative investigation (e.g. a clinical study or a large-scale research project) (e.g. in research-oriented courses of the degree programme or in the Master's thesis). In addition, they can classify the dangers and opportunities of "open data" and discuss them in an interdisciplinary manner. The students can also assess the strengths and weaknesses of qualitative and quantitative data-driven research and critically evaluate the methodological quality of the publications.

Prerequisite for participation There are no specific prerequisites.
Lecturer Nahnsen
Literature / Other

-

Last offered Sommersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas BIO-BIO, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, MEDZ-SEM, ML-CS



Module Number

BIOINF4210 (entspricht BIO-4210)
Module Title

Practical Transcriptomics
Lecture Type(s)

Practical Course
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

The final grade is based on performance, a written report on each day of the practical course, and one or two short oral presentations.

Content

The focus is on the practical analysis of so-called next generation sequencing
data. Students learn the use of tools for evaluating this data. This practical
course uses real-life data; the focus is on the entire process of evaluating experimental
data, from quality analyses to in-depth statistical analyses; various
methods are compared. Topics include de-novo assembly, expression count calculation,
normalization and clustering, machine learning methods and their
application to expression data, statistical methods for calculating differential
expressions, visualization methods, and enrichment methods.

Objectives

Students will gain practical experience in designing and programming bioinformatics
software for analyzing NGS data. They will be able to use libraries
and frameworks, and will acquire knowledge or extend their knowledge of Java
or C++ and R. By working together in groups, students obtain teamwork and
collaboration skills, and they will learn about project organization and presentation
techniques. Students will know about the strengths and weaknesses
and about the limitations of various methods for evaluating high-throughput
transcriptomic data, and will be able to describe and evaluate these methods.

Prerequisite for participation BIOINF3330 Expressions Bioinformatics,

BIOINF4110 (entspricht BIO-4110) Sequence Bioinformatics,

BIOINF4120 (enstpricht BIO-4120) Bioinformatics of Structures and Systems
Lecturer Nieselt
Literature / Other

Will be provided at the beginning of the course, if necessary.

Last offered unknown
Planned for currently not planned
Assigned Study Areas BIO-PRAK, MEDZ-BIOMED



Module Number

BIOINF4331 (entspricht BIO-4331)
Module Title

Advances in Computational Transcriptomics
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction English
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

Functional genomics, i.e. the interpretation of a genome to determine the biological
function of genes and gene interactions, is one of the most important
fields in modern biology. Today, next-generationßequencing technologies are increasingly
being used to measure the expression of thousands of genes simultaneously.
This results in new challenges for bioinformatics, both algorithmically
and software-wise. In the lecture the following topics will be discussed among
others: NGS technologies, in particular RNA-Seq and ChIP-Seq technologies,
fast to ultrafast alignment methods of short reads, mapping-based and de novo
’assembly’ of genomes and transcriptomes, peak calling, splicing and gene models,
motif search, differential expression, visualization of NGS data and other
current topics. In the exercises, especially scientific work and scientific writing
is encouraged. The exercises are also supplemented with blended learning
methods

Objectives

The students are familiar with the new bioinformatics findings on expression
analysis and the newer sequencing technologies. They can formulate the challenges
of the new technologies for bioinformatics. They know algorithms for
the quantification of expression data, statistical methods and machine learning
procedures for the calculation of differential expression and classification as well
as methods for the analysis of expression data in a network context. Students
can analyse real microarray experiments as well as RNA-Seq experiments and
have deepened their R knowledge. The students are aware of the possibilities
but also the limitations of different methods in this subfield of bioinformatics.
They are able to analyse problems on a scientific level and summarise them
in writing. In particular, a high degree of intrinsic motivation and personal
responsibility is encouraged.

Prerequisite for participation BIOINF3330 Expressions Bioinformatics
Lecturer Nieselt
Literature / Other

Own lecture notes and selected articles

Last offered unknown
Planned for currently not planned
Assigned Study Areas BIO-BIO, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, ML-CS



Module Number

BIOINF4363 (entspricht BIO-4363)
Module Title

RNA Bioinformatics
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Oral Presentation (about 30 minutes) and written elaboration (approx. 10 pages), leading the discussion once

Content

In this seminar, current topics related to computer-aided RNA bioinformatics
will be discussed. These can be, among others, the following: Folding:
RNA structure, thermodynamics, basic folding; RNA Abstract shapes; Comparative
Structure Prediction: structure comparison, alignment folding, consensus
shapes; Structure Comparison: structure metrics, tree alignment, multiple
structure alignment; RNA gene prediction: prediction from models, prediction
from folding, prediction from comparisons; miRNAs: miRNA prediction,
miRNA target prediction; Stochastic Models: HMMs, SCFGs, model training;
3D-Modelling; Cofolding; RNA Motifs and other topics supplemented by current
research.

Objectives

The students can independently work with supervision on a challenging topic
through systematic research. Students gain experience in giving a technical
presentation and producing a technical writeup in bioinformatics. They summarize,
assess, classify, scientifically correctly represent and present concepts
and methods of bioinformatic RNA biology. On the one hand, the students will
get an overview of modern knowledge in the field of bioinformatic RNA biology
and thus the importance of this subfield of bioinformatics. On the other hand,
they will know that there are still many open research questions in this field.
By studying current articles, the students have not only improved their reading
and learning skills, but also their personal responsibility. The form of learning
used in the seminar is intended to help the students to develop self-confidence
(presentation) and the ability to criticise and communicate (subsequent discussion).

Prerequisite for participation There are no specific prerequisites.
Lecturer Nieselt
Literature / Other

Articles / scientific publications for each individual topic

Last offered unknown
Planned for currently not planned
Assigned Study Areas BIO-BIO, BIO-SEM, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, MEDZ-SEM, ML-CS



Module Number

BIOINF4373 (entspricht BIO-4373)
Module Title

Bioinformatics and Machine Learning
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Oral Presentation (about 30 minutes) and written elaboration (approx. 10 pages), leading the discussion once

Content

In this seminar, machine learning approaches with applications to bioinformatics
will be discussed. These can be, among others, the following: supervised
classification; deep learning in bioinformatics, support vector machines
for classification; dimension reduction methods; probabilistic graphical models;
applications to the fields of genomics, transcriptomics, evolution, systems biology,
text mining and other topics supplemented by current research will be
discussed.

Objectives

The students can independently work with supervision on a challenging topic
through systematic research. They summarize, assess, classify, scientifically correctly
represent and present concepts and methods of machine learning that
are applied to bioinformatics problems. On the one hand, students will get an
overview of modern knowledge in the field of machine learning and their importance
for various questions in bioinformatics. On the other hand, students
will know that there are still many open research questions in this field. By
studying current articles, the students have not only improved their reading
and learning skills, but also their personal responsibility. The form of learning
used in the seminar is intended to help the students to develop self-confidence
(presentation) and the ability to criticise and communicate (subsequent discussion).

Prerequisite for participation There are no specific prerequisites.
Lecturer Nieselt
Literature / Other

Articles / scientific publications for each individual topic

Last offered unknown
Planned for currently not planned
Assigned Study Areas BIO-BIO, BIO-SEM, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, MEDZ-SEM, ML-CS



Module Number

BIOINF4365 (entspricht BIO-4365)
Module Title

Introduction to next-generation sequencing
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written Test

Content

inhalt 4365

Objectives

goals 4365

Prerequisite for participation There are no specific prerequisites.
Lecturer Ossowski
Literature / Other

-

Last offered unknown
Planned for currently not planned
Assigned Study Areas BIO-BIO, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, ML-CS



Module Number

INFO-4241
Module Title

Programming Languages II
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written or oral examination. Participation in exercises is required for exam participation.

Content

This lecture is about the semantics and type systems of modern programming
languages. We discuss the foundations of programming languages using formal
semantics (such as small-step operational semantics), formal type systems and
their properties, and different variants of typed lambda calculi that constitute
the foundation for modern type systems.

Objectives

Students will be able to discuss and analyze modern programming languages in
terms of the properties of their theoretical foundations. They will understand
the design space and tradeoffs of type systems for these languages.

Prerequisite for participation INF3181 Programming Languages I
Lecturer Brachthäuser, Ostermann
Literature / Other

Benjamin C. Pierce. Types and Programming Languages. MIT Press, 2003.

Last offered Wintersemester 2021
Planned for Sommersemester 2023
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4242
Module Title

Programming Languages III
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written Test

Content

info 4242

Objectives

goals 4242

Prerequisite for participation INF3181 Programming Languages I,

INFO-4241 Programming Languages II
Lecturer Ostermann
Literature / Other

-

Last offered -
Planned for -
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4481
Module Title

Topics in Programming Language Theory
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

To be announced.

Content

The seminar includes the elaboration of written sources on topics related to the theory of programming languages. Presentation and the written summary conclude the seminar work. Active participation in each session is an important part of the seminar.

The title of the seminar can vary depending on the semester and the concrete seminar topics. Please refer to the course catalogue in alma for the seminar offer in a specific semester.

Objectives

The students can independently develop and understand an extended and complex subject from the field of programming language theory from a written source and present it in the form of a lecture and also represent it in a discussion in front of a plenum. In addition to the oral presentation, they can present and summarise the elaborated topic in writing.

Prerequisite for participation INF3181 Programming Languages I,

INFO-4241 Programming Languages II
Lecturer Ostermann
Literature / Other

wird bekannt gegeben

Last offered unknown
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS



Module Number

INFO-4244
Module Title

Programming Languages and Techniques
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

Will be announced in the respective seminar,

Content

The seminar includes the development of written sources on topics related to programming and programming languages. Presentation and the written summary conclude the seminar work. Active participation in the individual sessions is an important part of the seminar.

For the respective seminar offers in a given semester please refer to the course catalogue in alma.

Objectives

The students can independently develop and understand an extended and complex subject from the field of programming techniques from a written source and present it in the form of a lecture and also represent it in a discussion in front of a plenum. In addition to the oral presentation, they can present and summarise the elaborated topic in writing.

Prerequisite for participation INF3181 Programming Languages I,

INFO-4241 Programming Languages II,

INFO-4242 Programming Languages III
Lecturer Ostermann, Plümicke
Literature / Other

wird bekannt gegeben

Last offered Wintersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS



Module Number

INFO-4245
Module Title

Software Project Management
Lecture Type(s)

Practical Course
ECTS 9
Work load
- Contact time
- Self study
Work load:
270 h
Class time:
90 h / 6 SWS
Self study:
180 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction German
Type of Exam

Graded reports and presentations as well as success in team leadership are included in the grade.

Content

In this internship, you will lead a group of students in the implementation of a software project as part of the "Tübingen Software Project". This includes the management of the project as well as the technical leadership, which includes aspects such as workflow configuration, social coding, quality management, continuous integration and testing. Through specialized training, including from our industry partners,in the aspects listed above, we will prepare you for this role. This internship will last for a full year.

Objectives

Participants are able to lead a small group of software developers and take over the technical and organisational management of a medium-sized software project.

Prerequisite for participation There are no specific prerequisites.
Lecturer Ostermann
Literature / Other

Alle Materialien werden bereitgestellt. / Erfahrungen mit einem größeren Softwareprojekt sind sehr hilfreich.

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4247
Module Title

Algorithmic Action
Lecture Type(s)

Lecture
ECTS 9
Work load
- Contact time
- Self study
Work load:
270 h
Class time:
90 h / 6 SWS
Self study:
180 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written Test

Content

inhalt 4247

Objectives

goals 4247

Prerequisite for participation There are no specific prerequisites.
Lecturer Ostermann
Literature / Other

-

Last offered -
Planned for -
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4248
Module Title

Interactive Theorem Proving
Lecture Type(s)

Lecture
ECTS 9
Work load
- Contact time
- Self study
Work load:
270 h
Class time:
90 h / 6 SWS
Self study:
180 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

Written or oral examination. Participation in exercises is required for exam participation.

Content

This course is an introduction to interactive theorem programming and advanced functional programming, mostly using the Coq proof assistant.
This course is for students interested in:
1. The foundational theories of mathematics, most notably type theory and logic
2. Practical interactive theorem proving in a state-of-the-art proof assistant
3. Advanced functional programming languages and their relation to constructive mathematics via the “Curry-Howard Isomorphism”
4. Program verification and “certified programming”
5. Programming Language Semantics

Objectives

Students will be able to write programs and prove theorems in the Coq proof assistant.
Students understand the theoretical underpinnings of interactive theorem
provers and get basic insights into the semantics and formal properties of
programming languages.

Prerequisite for participation There are no specific prerequisites.
Lecturer Ostermann
Literature / Other

Volume 1 and 2 of the “Software Foundations” series available at https://
softwarefoundations.cis.upenn.edu/.
A. Chlipala, Certified Programming with Dependent Types, MIT Press / A background in functional programming is helpful. Experience with mathematical
proofs is helpful.

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4249
Module Title

Advanced Topics in Programming Languages
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

Written exam (oral exam if the number of participants is small), points gained in the exercises may be transferable as bonus points into the exam.

Content

Changing in-depth topics from the subfields of the research field of programming languages.

Objectives

Students have knowledge of research methodology in the field of programming languages. They are prepared for writing scientific papers, especially in sub-areas of this research field. The students can prepare specifically for Master's theses and doctoral projects.

Prerequisite for participation There are no specific prerequisites.
Lecturer Ostermann
Literature / Other

wird bekannt gegeben

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4243
Module Title

Application of programming language technologies
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written Test

Content

info 4243

Objectives

goals 4243

Prerequisite for participation There are no specific prerequisites.
Lecturer Ostermann
Literature / Other

-

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS



Module Number

MEDZ-4110
Module Title

Advanced Medical Informatics
Lecture Type(s)

Lecture, Tutorial
ECTS 9
Work load
- Contact time
- Self study
Work load:
270 h
Class time:
90 h / 6 SWS
Self study:
180 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction English
Type of Exam

Written exam or oral exam

Content

This lecture comprises different areas of Medical Informatics. The focus is on
data integration, medical data privacy, artificial intelligence and data mining
for health data, and treatment decision support systems. Specific topics are:
• statistical machine learning basics
• state-of-the-art in decision support systems and beyond
• differential privacy
• k-anonymity
• privacy-preserving record linkage
• federated learning approaches and GO-FAIR
• genome privacy
• FHIR
• openEHR
• data warehouses and no-SQL data bases
• map reduce

Objectives

The students are capable of explaining the most important terms, methods and
theories of clinical decision support systems, medical data privacy, and data
integration and analysis. They are enabled to decide which type of methods fit
to which kind of data sets. The students can critically reflect on shortcomings
of state-of-the-art methods to potentially come up with ideas for extending or
improving the methods.

Prerequisite for participation MDZINFM1410 Introduction to Medical Informatics
Lecturer Pfeifer
Literature / Other

Eta S. Berner: Clinical Decision Support Systems - Theory and Practice, A.
Gkoulalas-Divanis and G. Loukides: Medical Data Privacy Handbook, P. Lake
and P. Crowther: Concise guide to databases / recommended: Grundlagen der Medizininformatik

Last offered Wintersemester 2022
Planned for Wintersemester 2023
Assigned Study Areas BIO-BIO, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, ML-CS



Module Number

MEDZ-4991
Module Title

Medical Data Science
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction English
Type of Exam

Written Test

Content

This lecture comprises different areas of Medical Data Science. Data Science or statistical machine learning methods have the potential to transform personal health care over the coming years. Advances in the technologies have generated large biological data sets. In order to gain insights that can then be used to improve preventive care or treatment of patients, these big data have to be stored in a way that enables fast querying of relevant characteristics of the data and consequently building statistical models that represent the dependencies between variables. These models can then be utilized to derive new biomedical principals, provide evidence for or against certain hypotheses, and to assist medical professionals in their decision process.

Specific topics are:
• Gaining new insights from medical data
• Modeling uncertainty in medical data science models
• Making medical findings available through interpretable decision support systems

Method-wise, the lecture introduces methods for GWAS analyses (e.g., LMMs), methods for sequence analysis (e.g., kernel methods), methods for “small n problems” (e.g., domain adaptation, transfer learning, and multitask learning),
methods for data integration (advanced unsupervised learning methods), methods for learning probabilistic Machine Learning models (e.g., graphical models), methods for large data sets (e.g., deep learning models).

Objectives

The students are capable of explaining the most important terms, methods and theories in the data science area with focus on the analysis of biomedical data. They are enabled to decide which type of methods fit to which kind of data sets. The students can critically reflect on shortcomings of state-of-the-art methods
to potentially come up with ideas for extending or improving the methods.

Prerequisite for participation There are no specific prerequisites.
Lecturer Pfeifer
Literature / Other

Trevor Hastie, Robert Tibshirani, Jerome Friedman: The Elements of Statistical
Learning, Springer Series in Statistics.
Further books will be announced in the first lecture. / recommended: Machine learning: theory and algorithms or Introduction to Statistical
Machine Learning for Bioinfos and Medicine Infos

Last offered Wintersemester 2022
Planned for Wintersemester 2023
Assigned Study Areas BIO-BIO, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, MEDZ-MEDTECH, ML-CS, ML-DIV



Module Number

MEDZ-4520
Module Title

Biomedical Informatics Methods for Infection Research
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Oral presentation and written report

Content

This seminar covers different aspects of biomedical informatics methods for
infection research. This includes computer science methods to support research
in the following areas:
• Pathogen-host interactions
• Diversity of pathogens and its relevance for human infections
• Analysis of viral epitopes
• Support for vaccine development
• Predicting drug resistance
• Assessing the efficacy of combination drug therapies

Objectives

Successful students know the most important terms, theories and methods in
the field of fighting infections with computer science methods and know how
to critically reflect on them.

Prerequisite for participation There are no specific prerequisites.
Lecturer Pfeifer
Literature / Other

The papers will be announced at the first meeting. / recommended: Machine learning: theory and algorithms or Introduction to Statistical
Machine Learning for Bioinfos and Medicine Infos

Last offered unknown
Planned for currently not planned
Assigned Study Areas BIO-BIO, BIO-SEM, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS



Module Number

MEDZ-4521
Module Title

Computer Science Methods for Privacy Preservation in Biomedical Studies
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Talk and Report

Content

This seminar covers different aspects of computer science methods for privacy preservation of medical data as well as for personalized medicine. This includes computer science methods to support research in the following areas:
• Privacy-preserving machine learning
• Secure multiparty computation
• Decision support

Objectives

Successful students know the most important terms, theories and methods in the field of privacy preservation with computer science methods and know how to critically reflect on them.

Prerequisite for participation There are no specific prerequisites.
Lecturer Pfeifer
Literature / Other

-

Last offered unknown
Planned for Wintersemester 2023
Assigned Study Areas BIO-BIO, BIO-SEM, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS



Module Number

MEDZ-4522
Module Title

Machine Learning for Health
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction English
Type of Exam

Oral presentation and written report

Content

This seminar covers different state-of-the-art machine learning methods on biomedical
data to answer medical questions of interest. This can include:
• Graphical model structure learning and causality in medicine
• Deep learning approaches in medicine
• Machine learning methods for small sample sizes

Objectives

Machine Learning for Health: Successful students know the most important terms, theories and methods in
the field of fighting infections with computer science methods and know how
to critically reflect on them.

Prerequisite for participation There are no specific prerequisites.
Lecturer Pfeifer
Literature / Other

The papers will be announced at the first meeting. / recommended: Machine learning: theory and algorithms or Introduction to Statistical
Machine Learning for Bioinfos and Medicine Infos

Last offered Wintersemester 2022
Planned for Wintersemester 2023
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS, ML-DIV



Module Number

MEDZ-4250
Module Title

Machine Learning in Biomedicine
Lecture Type(s)

Practical Course
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written Test

Content

inhalt medz 4251

Objectives

goals medz 4250

Prerequisite for participation There are no specific prerequisites.
Lecturer Pfeifer
Literature / Other

-

Last offered unknown
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDZ-BIOMED, MEDZ-MEDTECH, ML-CS, ML-DIV



Module Number

ML-4530
Module Title

Deep Learning for Vision and Graphics
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Oral Test

Content

The fields of 3D computer vision and graphics have been revolutionized by deep learning. For example, it is now possible to obtain detailed 3D reconstructions of humans and objects from single images, generate photo-realistic renderings of 3D scenes with neural networks, or manipulate and edit videos and images. In this seminar, we will cover the most recent publications and advances in the fields of neural rendering, 3D computer vision, 3D shape reconstruction, and representation learning for 3D shapes.

Objectives

Students are able to read and reflect upon current research papers in this research area. They can critically assess the contributions of such a paper. They can present current research results to other students and researchers and can lead research discussions.

Prerequisite for participation There are no specific prerequisites.
Lecturer Pons-Moll
Literature / Other

Will be announced in the first meeting / Programming skills, knowledge of linear algebra and calculus, numerical optimization, probability theory.
Prior participation in one of: Deep Learning, Probabilistic ML, Mathematics for ML, Statistical ML is required.

Last offered Sommersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS, ML-DIV



Module Number

INFO-4314
Module Title

Programming of Mobile Embedded Systems
Lecture Type(s)

Practical Course
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction German
Type of Exam

Final Project Presentation and Report

Content

This module provides hands-on experience in designing and programming mobile embedded systems (ES). Participants will work in teams of up to three students and in three groups to develop a platform for a small network. The network consists of the following fixed and mobile nodes that communicate wirelessly using Bluetooth technology: A sensor/actuator node programmable in C programming language with an AVR processor. A programmable mobile phone with Bluetooth capability, programmable in Java2ME. A PC as a fixed node with Bluetooth hardware, to be programmed in Java2SE. Students will be provided with a specification of the system to be developed and will independently prepare all development documentation under supervision. Students will learn to design, program and debug a client/server system. During the internship, students are supported by experienced tutors. The internship is highly structured. Weekly tasks are assigned according to a set schedule and their solutions must be demonstrated on time.

Objectives

Students can systematically develop software for embedded systems. They know the entire development process from specification, through development, to debugging and documentation. The students can use proven development environments such as Eclipse, Netbeans, Subversion and the team communication system TRAC. The practical course is completed in small groups. Emphasis is placed on teamwork, communication within and between groups, systematic problem solving and meeting deadlines. This promotes students' self-confidence, self-marketing skills and ability to deal with conflict.

Prerequisite for participation There are no specific prerequisites.
Lecturer Bringmann
Literature / Other

• M. Sauter. Grundkurs Mobile Kommunikationssysteme
• Kumar et al. Java Programming with Bluetooth
• Internet-Hilfen für die Entwicklungssysteme Eclipse und Netbeans

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS



Module Number

INFO-4321
Module Title

Enterprise Computing - Foundation
Lecture Type(s)

Lecture
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction German
Type of Exam

Written Test

Content

The course provides students with an overview of the IT systems and facilities that large corporations and government organizations use to manage their daily IT operations. The content of the module is mainly based on the use of modern mainframe computers and the mainframe technology used. In detail, the course covers the following topics: Overview, Processing Basics, z-Systems Architecture and Hardware, Firmware and RAS, z/OS Operating System, Input/Output Processing, Data Organization, Virtualization and System Management, Unix and Linux on z-Systems, Clustering and Sysplex, IT Infrastructure.

Objectives

The students master the learned system structures and computer architectures as they are used by large companies and can apply them in a problem-oriented manner. This enables the students to apply the advantages and disadvantages of these IT methods to new scenarios in industrial practice in a situation-appropriate manner and to expand them competently. Furthermore, the students can justify and present the necessary system decisions.

Prerequisite for participation There are no specific prerequisites.
Lecturer Kreißig, Schmidt
Literature / Other

• U. Kebschull, P. Herrmann, W.G. Spruth. Einführung in z/OS und
OS/390. 2. Auflage, Oldenbourg 2004, ISBN 3-486-27393-0.
• M. Teuffel, R. Vaupel. Das Betriebssystem z/OS und die zSeries. Oldenbourg
2004., ISBN 3-486-27528-3
• W. Greis. Die IBM-Mainframe-Architektur. Open Source Press, 2005,
ISBN 3-937514-05-8.
• W. Zack. Windows 2000 and Mainframe Integration. Macmillan Technical
Publishing, 1999., ISBN 978-1-57870-200-8
• M. Teuffel. TSO : Time Sharing Option im Betriebssystem z/OS MVS.
7. Aufl. München : Oldenbourg Wissenschaftsverlag, 2001. – ISBN 978-
3-48625560-7
• Lawson, Susan ; Luksetich, Daniel: DB2 10 for z/OS Database Administration
: Certification Study Guide. Ketchum : MC Press, 2012. – ISBN
978-1-58347-369-6
• S.G. Sloan, A.K. Hernandez. An Introduction to DB2 for OS/390. Prentice
Hall, 2001.
• IBM Redbooks (http://www.redbooks.ibm.com).

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS



Module Number

INFO-4322
Module Title

Enterprise Computing - Practical Course
Lecture Type(s)

Practical Course
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction German
Type of Exam

Elaboration and presentation of the internship tasks

Content

File management under TSO, development of a COBOL batch program, VSAM file organization, DB2

Objectives

The students are able to apply the knowledge learned in the module Enterprise Computing and to deepen and expand it independently accordingly. They are able to see through, analyse and solve practical problems accordingly. Thus, at the end of their studies, the students are able to process tasks in industrial practice in a result-oriented and competent manner. The tasks set in this module are worked on in small groups. This trains cooperation, communication and conflict skills as well as self-discipline and a sense of responsibility.

Prerequisite for participation INFO-4321 Enterprise Computing - Foundation
Lecturer Kreißig
Literature / Other

• Herrmann, Paul ; Spruth, Wilhelm G.: Einführung in z/OS und OS/390 : Web-Services und Internet-Anwendungen für Mainframes. 3. Aufl. München : Oldenbourg Wissenschaftsverlag, 2012. – ISBN 978-3-48670428-0
• Teuffel, Michael ; Vaupel, Robert: Das Betriebssystem z/OS und die zSeries : Die Darstellung eines modernen Großrechnersystems. München : Oldenbourg Wissenschaftsverlag, 2004. – ISBN 978-3-48627528-5
• W. Greis. Die IBM-Mainframe-Architektur. Open Source Press, 2005, ISBN 3-937514-05-8.
• Zack, William H.: Windows 2000 and Mainframe Integration. New York : Macmillan Technical Publishing, 1999. – ISBN 978-1- 57870-200-8
• Ben-Natan, Ron ; Sasson, Ori: IBM WebSphere Starter Kit. New York : McGraw-Hill Professional, 2000. – ISBN 978-0-07-212407- 1
• Lawson, Susan ; Luksetich, Daniel: DB2 10 for z/OS Database Administration : Certification Study Guide. Ketchum : MC Press, 2012. – ISBN 978-1-58347-369-6
• IBM Redbooks (http://www.redbooks.ibm.com)

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS



Module Number

INFO-4323
Module Title

Enterprise Computing - Applications
Lecture Type(s)

Lecture
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction German
Type of Exam

Written Test

Content

This course provides students with an overview of the range of applications and the middleware services they require to meet the diverse customer needs of large enterprises and government organizations. The focus of these courses is the description of the different software components and their interaction. In detail, the event covers the following topics: Industrial Mainframe Application and Transaction Processing, CICS, Mainframe Database Management System, System Communication, Analytics and BigData, Secure Container and Blockchain, Workload Management, Java on Mainframes, Web Application Server, Software Development in Large Enterprises.

Objectives

Students learn the requirements for operating complex software environments and applications that large companies and government institutions use to manage their daily operations. This enables them to analyse these software environments and define possible extensions and additions. Furthermore, the students can present and evaluate these concepts to decision-makers. Overall, the students are able to apply the advantages and disadvantages of these IT methods to new scenarios in industrial practice in a situation-appropriate manner and to expand them competently.

Prerequisite for participation There are no specific prerequisites.
Lecturer Kreißig, Schmidt
Literature / Other

• Herrmann, Paul ; Spruth, Wilhelm G.: Einführung in z/OS und OS/390 : Web-Services und Internet-Anwendungen für Mainframes. 3. Aufl. München : Oldenbourg Wissenschaftsverlag, 2012. – ISBN 978-3-48670428-0
• Teuffel, Michael ; Vaupel, Robert: Das Betriebssystem z/OS und die zSeries : Die Darstellung eines modernen Großrechnersystems. München : Oldenbourg Wissenschaftsverlag, 2004. – ISBN 978-3-48627528-5
• Zack, William H.: Windows 2000 and Mainframe Integration. New York : Macmillan Technical Publishing, 1999. – ISBN 978-1- 57870-200-8
• Teuffel, Michael: TSO : Time Sharing Option im Betriebssystem z/OS MVS. 7. Aufl. München : Oldenbourg Wissenschaftsverlag, 2001. – ISBN 978-3-48625560-7
• Horswill, John: Designing and Programming CICS Applications. Sebastopol : O’Reilly & Associates, 2000. – ISBN 978-1-56592- 676-9
• Ben-Natan, Ron ; Sasson, Ori: IBM WebSphere Starter Kit. New York : McGraw-Hill Professional, 2000. – ISBN 978-0-07-212407- 1
• IBM Redbooks: http://www.redbooks.ibm.com/

Last offered unknown
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS



Module Number

INFO-4324
Module Title

Enterprise Computing - Applications Practical Course
Lecture Type(s)

Practical Course
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction German
Type of Exam

Elaboration and presentation of the internship tasks

Content

COBOL unter CICS, CICS Transaction Gateway, IBM Rational Developer für z Systems, Java Remote Method Invocation

Objectives

The students are able to apply the knowledge learned in the module Enterprise Computing, to deepen it accordingly and to extend it independently. They can see through, analyse and solve practical problems. Thus, at the end of their studies, the students are able to process tasks in industrial practice in a results-oriented and competent manner. The tasks set in this module are worked on in small groups. In addition to cooperation, communication and conflict skills, this also trains self-discipline and a sense of responsibility.

Prerequisite for participation INFO-4323 Enterprise Computing - Applications
Lecturer Kreißig
Literature / Other

• Herrmann, Paul ; Spruth, Wilhelm G.: Einführung in z/OS und OS/390 : Web-Services und Internet-Anwendungen für Mainframes. 3. Aufl. München
: Oldenbourg Wissenschaftsverlag, 2012. – ISBN 978-3-48670428-0
• Teuffel, Michael ; Vaupel, Robert: Das Betriebssystem z/OS und die zSeries : Die Darstellung eines modernen Großrechnersystems. München : Oldenbourg Wissenschaftsverlag, 2004. – ISBN 978-3-48627528-5
• Zack, William H.: Windows 2000 and Mainframe Integration. New York : Macmillan Technical Publishing, 1999. – ISBN 978-1- 57870-200-8
• Teuffel, Michael: TSO : Time Sharing Option im Betriebssystem z/OS MVS. 7. Aufl. München : Oldenbourg Wissenschaftsverlag, 2001. – ISBN 978-3-48625560-7
• Horswill, John: Designing and Programming CICS Applications. Sebastopol : O’Reilly /& Associates, 2000. – ISBN 978-1-56592- 676-9
• Ben-Natan, Ron ; Sasson, Ori: IBM WebSphere Starter Kit. New York : McGraw-Hill Professional, 2000. – ISBN 978-0-07-212407- 1
• IBM Redbooks: http://www.redbooks.ibm.com/

Last offered unknown
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS



Module Number

INFO-4374
Module Title

Software Quality
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction German
Type of Exam

Presentation

Content

The reliability, safety, correctness and robustness of embedded systems are becoming increasingly important. Errors occur again and again, including critical ones that are due to thought-logical errors in the specification and implementation of the system. Hardware and software are as important as the hardware description languages and programming languages used. For the avoidance of errors restrictions are often specified to the used languages, in order to prevent dynamic malfunctions, but also, in order to simplify the analysis and verification. The techniques range from static analysis of systems, programs and specifications with respect to a wide variety of issues to more and more combinations of machine proof systems and model checkers. In addition to fault avoidance, fault tolerance (e.g., through redundancy, multiple design) is also an interesting approach for software. Techniques such as runtime checking, observer processes, monitoring, consistency checking are used. More and more, the focus is on quality testing and guaranteeing properties of the systems. An example would be e.g. the certification of safety-relevant systems. In this connection also libraries, tools, compilers, system components, foreign software are of importance, for which the manufacturers are likewise responsible. Mastering these complex interrelationships is not only relevant for systems that pose a risk to human life and limb, but also in the case of economic risk potential, e.g. in the area of security. The goal of this seminar is to give an insight into the theory of embedded system verification and the current tools developed in research, without losing focus on the industrial methods used today.

Objectives

The students can research scientific literature and have acquired reading and learning skills. They can prepare a topic in a structured manner and present it in writing and in the form of a lecture.

Prerequisite for participation There are no specific prerequisites.
Lecturer Kropf
Literature / Other

aktuelle Veröffentlichungen aus Industrie und Forschung

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDZ-SEM, ML-CS



Module Number

INFO-4661
Module Title

Computer Engineering (Seminar)
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction German
Type of Exam

Presentation

Content

Changing topics on technologies and methods from the research-oriented, scientific environment of computer engineering. Please note announcements and notices.

Objectives

Students are able to understand a complex, scientific issue from written sources, process it and present it independently in the form of a lecture with discussion and summarise it in a well-structured paper they have prepared themselves.

Prerequisite for participation There are no specific prerequisites.
Lecturer
Literature / Other

wird in der Vorbesprechung bekannt gegeben

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDZ-SEM, ML-CS



Module Number

INFO-4399
Module Title

Advanced Topis in Computer Engineering
Lecture Type(s)

Seminar
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Presentation

Content

This module deals with current topics in the field of technical computer science. These are brought to the students by means of current literature from research and industry. The module is primarily aimed at students who wish to acquire advanced knowledge in this area.

Objectives

The students are able to recognise, describe and evaluate current topics in technical computer science. By working on the topics independently, they have trained self-discipline as well as reading and learning skills of the students. Moderation competence, rhetoric and critical ability of the students are particularly improved by presenting the topic in front of an expert audience.

Prerequisite for participation There are no specific prerequisites.
Lecturer Bringmann
Literature / Other

Aktuelle Literatur, die in der Vorbesprechung bekannt gegeben wird.

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDZ-MEDTECH, MEDZ-SEM, ML-CS



Module Number

INFO-4191
Module Title

Neuronal Computing
Lecture Type(s)

Lecture
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction German
Type of Exam

Oral examination (written exam if there are a large number of participants)

Content

Within the module Neuronal Computing one of the best organized and most efficient systems to the computer will be presented: the biological neuronal network. In a first step, methods for communication with this computer system will be shown. Starting from information theory, methods for recording neuronal signals and their signal processing will be treated. First, different methods for recording nerve signals and the problems arising with them are treated from the point of view of signal processing. Afterwards methods for signal processing of nerve signals (spike sorter etc.) are presented. In particular, the current methods such as the JPSH (Joint Peri-Stimulus Histogram) or ISC (Inca-SOM-Clusot) will be discussed. The course is divided into Information Theory, Neurons as Computers, Networked Neurons, Recording Techniques, Signal Processing of Neural Signals, Modular/ Population Coding, Unitary Events Analysis, and Applications.

Objectives

The students have a deep scientific insight into neural computing based on current publications. They are able to transfer findings from biological systems and medicine directly into the field of computer science. This transfer performance requires a high degree of reading and learning competence and a high level of commitment to independent scientific information retrieval.

Prerequisite for participation There are no specific prerequisites.
Lecturer Nagel
Literature / Other

aktuelle Veröffentlichungen

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4192
Module Title

Machine Learning and Artificial Neuronal Networks in Biomedical Applications
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Presentation

Content

The seminar "Machine Learning and Artificial Neural Networks in Biomedical Applications" deals with current topics in signal processing in the field of nerve signal processing (e.g. neuroprosthetics or brain-computer interfaces), medical signals (e.g. fMRI or MEG) or related areas as well as signal processing algorithms used in these areas.

Objectives

The students have a deep scientific insight into neural computing based on current publications. They are able to transfer findings from biological systems and medicine directly into the field of computer science. This transfer performance requires a high degree of reading and learning competence and a high level of commitment to independent scientific information retrieval.

Prerequisite for participation There are no specific prerequisites.
Lecturer Nagel
Literature / Other

aktuelle Veröffentlichungen

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-MEDTECH, MEDZ-SEM, ML-CS



Module Number

INFO-4161
Module Title

Image Processing II (3D-Computer-Vision)
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction German
Type of Exam

Written exam (oral exam if the number of participants is small), grade can be improved by practice points (bonus). Minimum score in exercises required for admission to the exam.

Content

Topics include: Feature Point Extraction, Correlation and Matching, Epipolar Constraint, Fundamental Matrix, Camera Position Calculation, Image Warping, Optical flow and Dense Correspondence Matching.

Objectives

The students know the basic procedures for reconstructing 3D scenes from images and video recordings and can implement them.

Prerequisite for participation There are no specific prerequisites.
Lecturer Schilling
Literature / Other

Vorlesungsfolien werden zum Download bereitgestellt

Last offered Sommersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-MEDTECH, ML-CS



Module Number

INFO-4162
Module Title

Image Processing II (3D-Computer-Vision)
Lecture Type(s)

Practical Course
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction German
Type of Exam

Project, presentation and elaboration

Content

Implementation of programms from the field of computer vision.

Objectives

The students can independently (in small groups) plan and create programmes to solve simple problems of 3D reconstruction from images and apply their theoretical knowledge.

Prerequisite for participation INFO-4161 Image Processing II (3D-Computer-Vision)
Lecturer Schilling
Literature / Other

Entwicklungsumgebung wird zur Verfügung gestellt

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-MEDTECH, ML-CS



Module Number

INFO-4163
Module Title

Medical Image Processing
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

Processing of image and volume data in medicine: imaging techniques, X-ray, CT, MR, PET, radon transformation, filtering of 2D and 3D data, segmentation in 2D and 3D, visualization of voxel-based volume data, atlases and statistical models.

Objectives

The students know the important imaging procedures in medicine and understand the underlying technical and physical processes. They know basic algorithms for further processing and presentation of the acquired data.

Prerequisite for participation There are no specific prerequisites.
Lecturer Schilling
Literature / Other

Vorlesungsfolien werden zum Download bereitgestellt

Last offered -
Planned for -
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-MEDTECH, ML-CS



Module Number

INFO-4164
Module Title

Medical Image Processing
Lecture Type(s)

Practical Course
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German
Type of Exam

Project, presentation and elaboration

Content

Implementation of programs from the field of image processing, e.g. segmentation of X-ray data, visualization of voxel data

Objectives

Students can independently (in small groups) plan and implement programmes to solve simple problems in medical image processing, applying their theoretical knowledge.

Prerequisite for participation INFO-4163 Medical Image Processing
Lecturer Schilling
Literature / Other

Entwicklungsumgebung wird zur Verfügung gestellt

Last offered -
Planned for -
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-MEDTECH, ML-CS



Module Number

INFO-4170
Module Title

Geometric Modelling and Simulation
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German
Type of Exam

Written exam (oral exam if the number of participants is small), grade can be improved by practice points (bonus). Minimum score in exercises required for admission to the exam.

Content

Generation of polygon meshes, point data processing (laser scanning, registration,... ) Point-based representations, efficient mesh data structures, mesh compression, remeshing, hierarchical structures, mesh simplification.

Objectives

Students know the basic methods and algorithms for optimising, processing and storing geometric data. They are able to implement current algorithms for geometry processing.

Prerequisite for participation There are no specific prerequisites.
Lecturer Schilling
Literature / Other

Eigene Materialien und Vorlesungsfolien werden zum Download bereitgestellt

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-MEDTECH, ML-CS



Module Number

INFO-4172
Module Title

Virtual Reality
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German
Type of Exam

Written exam (oral exam if the number of participants is small), grade can be improved by practice points (bonus). Minimum score in exercises required for admission to the exam.

Content

Scene graphs, stereo (HW, SW), tracking (HW, SW), acceleration techniques (LOD; culling), collision detection, haptics, sound, GPU programming

Objectives

The students know hardware and software components of current VR systems and have a broad knowledge of algorithms from the areas of acquisition, simulation and rendering that are relevant for VR systems. They are able to implement components of a VR system.

Prerequisite for participation There are no specific prerequisites.
Lecturer Schilling
Literature / Other

-

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-MEDTECH, ML-CS



Module Number

INFO-4179
Module Title

Special Topics in Computer Graphics
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German
Type of Exam

Written exam (oral exam if the number of participants is small), grade can be improved by practice points (bonus). Minimum score in exercises required for admission to the exam.

Content

Special topics from the field of graphic data processing.

Objectives

The students acquire in-depth knowledge in special areas of graphical data processing, which are important e.g. for doctoral projects in the field of work. They can classify and evaluate new approaches. They are able to independently develop and implement new algorithms in the special field.

Prerequisite for participation There are no specific prerequisites.
Lecturer Schilling
Literature / Other

Vorlesungsfolien werden zum Download bereitgestellt

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-MEDTECH, ML-CS



Module Number

INFO-4187
Module Title

Image Processing, Machine Learning and Computer Vision
Lecture Type(s)

Practical Course
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

Project, presentation and elaboration

Content

Implementation of problem solving with algorithms from the fields of image processing, machine learning and computer vision.

Objectives

Students can independently (in small groups) use suitable algorithms and methods from the fields of image processing, machine learning and computer vision to solve concrete problems and combine methods from the different fields.

Prerequisite for participation There are no specific prerequisites.
Lecturer Schilling
Literature / Other

Entwicklungsumgebung wird zur Verfügung gestellt

Last offered Sommersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-MEDTECH, ML-CS



Module Number

INFO-4168
Module Title

Advanced Topics of Computer Graphics, Computer Vision and Machine Learning
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Presentation and written report

Content

Advanced topics in graphic data processing and computer vision, rendering algorithms, rendering hardware, computer vision and pattern recognition, classification, modeling, learning techniques in computer graphics and computer vision.

Objectives

Students are able to develop an advanced topic from the field of graphical data processing on the basis of current conference papers and journal articles, present and discuss it in front of the group and present the essentials in an understandable and correct way in a written paper.

Prerequisite for participation There are no specific prerequisites.
Lecturer Lensch, Schilling
Literature / Other

Hängen von den aktuellen Themen ab und werden zur Verfügung gestellt

Last offered unknown
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-SEM, ML-CS, ML-DIV



Module Number

MEDI-4510
Module Title

Audiovisual Media I (Camera and Digital Editing)
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction German
Type of Exam

Workpiece with documentation

Content

In the module, students first learn how to use professional video cameras in a practical way and deal with central issues of image and lighting design. Following digital video editing techniques using Adobe Premiere. they learn digital video editing techniques using Adobe Premiere. The content includes: integration of static and moving images, title generation, cross-fade effects, use of keyframes, encoding and file formats, file export.

Objectives

Students have knowledge of the basic techniques of digital video editing as well as the basics of using a video camera and lighting technology.

Prerequisite for participation There are no specific prerequisites.
Lecturer Schilling
Literature / Other

Katz: Die richtige Einstellung. Das Lehrbuch über Bildsprache und Filmgestaltung,
Schneeberger / Feix: Adobe Premiere Pro CS5, Vineyard: Crashkurs
Filmauflösung

Last offered unknown
Planned for Wintersemester 2023
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-PRAX, MEDZ-SEM, ML-CS



Module Number

MEDI-4511
Module Title

Audiovisual Media II (3D-Animation)
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction German
Type of Exam

Workpiece with documentation

Content

In the module, students learn basic 3D animation techniques. The focus is on 3D modeling and key frame animation. Common 3D animation software is used. Students learn the basics of animated film and technical animation in a very practical way using their own 3D workpieces. The certificate of achievement consists of a short 3D animation film, which the students create in small groups.

Objectives

Students have knowledge of basic 3D animation techniques, in particular 3D modelling and keyframe animation. They can actively implement this knowledge with common 3D animation software. The students acquire this knowledge in a practical way using their own workpieces.

Prerequisite for participation There are no specific prerequisites.
Lecturer Schilling
Literature / Other

Flavell: Beginning Blender: Open Source 3D Modeling, Animation, and Game Design,
Withrow: Secrets of Digital Animation: A Master Class in Innovative Tools and Techniques

Last offered unknown
Planned for Wintersemester 2023
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-PRAX, MEDZ-SEM, ML-CS



Module Number

MEDI-4512
Module Title

Audiovisual Media III (Special Effects)
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction German
Type of Exam

Workpiece with documentation

Content

Students work out and discuss central special effects and trick techniques of film productions on the basis of current examples. At the same time, they learn basic special effects, which they are to implement in small groups in form of a short film. Examples of effects include: Bluescreen/Greenscreen, overlay, digital matte, miniature effects, stop motion.

Objectives

Students have knowledge and in-depth understanding of the basic special effects of film production and are familiar with current effects techniques based on relevant, current examples.

Prerequisite for participation MEDI-4510 Audiovisual Media I (Camera and Digital Editing)
Lecturer Schilling
Literature / Other

Fontaine: Adobe After Effects CS5: Das Praxisbuch zum Lernen und Nachschlagen,
Giesen / Mulack: Special Visual Effects: Planung und Produktion,
Rickitt / Harryhausen: Special Effects: The History and Technique

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-PRAX, MEDZ-SEM, ML-CS



Module Number

MEDI-4599
Module Title

Media Production
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction German
Type of Exam

Written elaboration and presentation based on it

Content

Changing topics from the field of media production are presented by external lecturers from media and companies and introduced through practical projects. Examples are the topics typography and layout or sound engineering.

Objectives

Students possess in-depth knowledge and skills in a specific area of media production.

Prerequisite for participation There are no specific prerequisites.
Lecturer Schilling
Literature / Other

Aktuelle Literatur, die in der Veranstaltung bekannt gegeben wird.

Last offered Sommersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-PRAX, MEDZ-SEM, ML-CS



Module Number

INFO-4465
Module Title

Lambda Calculus and Combinatorial Logic
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written Test

Content

inhalt 4465

Objectives

goals 4465

Prerequisite for participation There are no specific prerequisites.
Lecturer Piecha
Literature / Other

-

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4492
Module Title

Special Topics in Learning Theory
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

In this module we discuss advanced results and approaches in learning theory
and current research results in the area of machine learning in general.

Objectives

Students get to know about advanced results in learning theory. They can judge
whether an algorithm is well designed, both from an algorithmic and statistical
point of view. They understand about the fundamental limitations of machine
learning. They can reflect current research questions. After this module they
are well-prepared to write a master thesis in the area of learning theory.

Prerequisite for participation There are no specific prerequisites.
Lecturer von Luxburg
Literature / Other

will be announced in the lecture

Last offered -
Planned for -
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV



Module Number

INFO-4493
Module Title

Learning Theory
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

Presentation and written report

Content

In this seminar we discuss current research papers in the area of machine learning
theory, in the form of student’s presentations and guided discussions.

Objectives

Students are able to read and reflect upon current research papers in the area
of learning theory. They can critically assess the contributions of such a paper.
They can present current research results to other students and researchers and
can lead research discussions. They can summarize and evaluate the results of
a paper in form of a written research report.

Prerequisite for participation There are no specific prerequisites.
Lecturer von Luxburg
Literature / Other

will be announced in the lecture / Basic knowledge in machine learning.

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS, ML-DIV



Module Number

MEDI-4310
Module Title

Advanced Web-Engineering
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

Content of this module are methods and techniques for the development of complex web applications. On the one hand, the possibilities of JEE are covered, first the basic approaches of servlets and JSP, then EJB, JSF and Hypernate. Furthermore, the possibilities of the dotnet framework based on C# will be covered. In addition to the pure development of advanced web applications, their operation in the corresponding server structures as well as the evaluation of the approaches with regard to stability, performance and development effort are also covered.

Objectives

Students have knowledge of the techniques for developing advanced web applications, especially in JEE framework and dotnet. They are able to independently design and implement advanced web applications, assessing and actively implementing different design patterns.

Prerequisite for participation There are no specific prerequisites.
Lecturer Walter
Literature / Other

T. Walter: Kompendium der Web-Programmierung, Springer, 2008

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-WEB, ML-CS



Module Number

MEDI-4320
Module Title

Advanced Media Application in the Internet
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written exam (oral exam if the number of participants is small) 80%, exercises 20%

Content

Methods of digital audio and video generation, detectors, physical basics, sampling, encoding methods, hardware accelerated encoding, multimedia transmission protocols, IP-based multimedia transmission protocols, media data transmission security, basics of media players, integration of audio and video in web applications - WebRTC, web sockets, html5. Media container formats - mp3, mp4. Practical components: Video conferencing in daily use, the lecture hall as a video studio, the realtime and on demand media server system of the University of Tübingen TIMMS.

Objectives

Students will understand and learn basic and advanced concepts and procedures of audio and video information generation, encoding, transmission and computer-aided presentation.

Prerequisite for participation There are no specific prerequisites.
Lecturer Walter
Literature / Other

Grundkenntnisse der Protokolle des Internet-Protokol-Stacks und der Netzwerkprogrammierung, Grundkenntnisse einer objektorientierten Programmiersprache

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-WEB, ML-CS



Module Number

MEDI-4330
Module Title

Digital Photography for the Web
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German
Type of Exam

Written exam (oral exam if the number of participants is small) 80%, exercises 20%

Content

The content of this module is the principles of analog and digital photography. This includes in particular the physical and chemical fundamentals. Building on this, the digitization of the image and the various possible formats for the web as well as basic image processing are covered. Bayer mosaic resolution for color will be discussed. Digital watermarks (robust and fragile) and forms of presentation on the web are topics of the module and legal aspects.

Objectives

The students understand the physical principles of analogue and especially digital photography and can implement the common techniques. They are actively familiar with image processing in general and especially for the target platform web, as well as the algorithmic resolution of the Bayer Mosaic. They can answer basic legal questions (copyright, image rights).

Prerequisite for participation There are no specific prerequisites.
Lecturer Walter
Literature / Other

T. Walter: MediaFotografie, Springer, 2005

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-PRAX, MEDI-WEB, ML-CS



Module Number

MEDI-4399
Module Title

Selected Topics in Web and Internet
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

Changing topics from the field of web development and multimedia. Exercises are held in small groups.

Objectives

Competences: Students possess in-depth knowledge and skills in a specific area of web development and multimedia from current research and development. They can actively realise these skills conceptually and in implementation. They can actively present their solutions.

Prerequisite for participation There are no specific prerequisites.
Lecturer Walter
Literature / Other

Aktuelle Literatur, die in der Veranstaltung bekannt gegeben wird.

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-WEB, ML-CS



Module Number

INFO-4151
Module Title

Applied Statistics II
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction German
Type of Exam

Written Test

Content

Building on "Applied Statistics I" (german: "Angewandte Statistik I"), more complex statistical methods are covered: Generalized Linear Models (GLM), Principal Component Analysis (PCA), Independence Analysis (ICA), and Bayesian statistics. The emphasis is on the practical application of all methods and their implementation in Python (with the modules statsmodels, scipy.stats, sklearn and pymc) and the presentation of results in notebooks.

Objectives

The students know advanced statistical methods, how to use them and how to implement them in software. They can figure out the differences between frequentist and Bayesian statistics.
The students are able to plan and evaluate experiments themselves and to avoid typical errors in experimental design. They can critically evaluate the way statistical methods are and results are presented in the literature.

Prerequisite for participation INF3223 Applied Statistics I,

INFM1010 Mathematics for Computer Science 1: Analysis,

INFM1020 Mathematics for Computer Science 2: Linear Algebra
Lecturer Wannek
Literature / Other

Wird in der Vorlesung bekannt gegeben

Last offered Sommersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4166
Module Title

Psychophysical Methods
Lecture Type(s)

Lecture
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction English
Type of Exam

Written Test

Content

The lecture covers the following topics:
Linear systems theory, psychophysical methods and experimental design, signal detection theory, diffusion models for reaction times, psychometric function estimation.

Objectives

Students learn the central behavioural limits, concepts and psychophysical methods in sensory psychology.

Prerequisite for participation There are no specific prerequisites.
Lecturer Wichmann
Literature / Other

Literatur / Literature:
Wird zu Beginn der Vorlesung angekündigt. / Will be announced at the beginning of the lecture.

Teilnahmevoraussetzungen / Prerequisites:
Grundlagenwissen in Mathematik und Statistik; empfohlen wird zudem Grundlagenwissen über die Prinzipien visueller Wahrnehmung. / Basic knowledge in mathematics and statistics is required; basic knowledge of the fundamentals of visual perception is strongly recommended.

Last offered Wintersemester 2022
Planned for Wintersemester 2023
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, ML-CS



Module Number

INFO-4169 (MKOGW1)
Module Title

Sensory Psychology
Lecture Type(s)

Lecture, Seminar
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 2 Semester
Frequency Each semester
Language of instruction English
Type of Exam

Written exam (Lecture), Oral presentation (seminar)

Content

The module consists of 2 parts:
1) the lecture "Psychophysical Methods" (every winter term)
2) one of three seminars at choice ("Spatial Vision", "Colour Vision & Material Perception", or "Theories of Vision"), with each seminar being offered triennally in the summer term.

To complete the module, students have to take the compulsory lecture "Psychophysical Methods" first (in the winter term), followed by one of the three seminars (in the summer term).

The contents of the lecture and the seminars are:
1) Lecture "Psychophysical Methods":
Linear systems theory, psychophysical methods and experimental design, signal detection theory, diffusion models
for reaction times, psychometric function estimation.

2a) Seminar "Spatial Vision": Optics of the eye, absolute thresholds, adaptation, contrast sensitivity function, spatial frequency selectivity, contrast gain-control, early visual representation of the world.

2b) Seminar "Colour Vision & Material Perception": Spectral composition of light, wavelength encoding, colour matching, trichromacy, colour appearance, colour constancy, material properties & perception.

2c) Seminar "Theories of Vision": Inverse optics, Gibson’s direct perception, vision as (unconscious) inference, the interface theory of vision, vision as predictive coding, the efficient coding hypothesis.

Objectives

The students can classify and critically reflect the central behavioural limits, concepts and psychophysical methods in sensory psychology. They have in-depth knowledge of the state-of-the-art models and their theoretical foundations in one specific subdomain of sensory psychology (spatial vision,
colour vision, material perception or theories of vision). They have consolidated their ability to discuss, critically reflect and present scientific work.

Prerequisite for participation There are no specific prerequisites.
Lecturer Wichmann
Literature / Other

Literatur / Literature: Wird zu Beginn jeder Veranstaltung angekündigt. / Will be announced at the beginning of each course.

Teilnahmevoraussetzungen / Prerequisites: Grundlagenwissen in Mathematik und Statistik; dringend empfohlen wird zudem Grundlagenwissen über die Prinzipien visueller Wahrnehmung. / Basic knowledge in mathematics and statistics is required; basic knowledge of the fundamentals of visual perception is strongly recommended.

Last offered Wintersemester 2022
Planned for Sommersemester 2023
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-SEM, ML-CS



Module Number

ML-4710
Module Title

Beyond Fairness: a Socio-Technical view of Machine Learning
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written Test

Content

inhalt ml 4710

Objectives

goals ml 4710

Prerequisite for participation There are no specific prerequisites.
Lecturer Williamson
Literature / Other

-

Last offered -
Planned for -
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV



Module Number

INFO-4181
Module Title

Pattern Recognition and Machine Learning
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

To be announced.

Content

The module covers the first chapters of the textbook by Ch. Bishop mentioned below: Introduction to Machine Learning, probability distributions, linear models for regression, linear models for classification, neural networks (short), kernel methods, mixture models and EM algorithms.

Objectives

Students acquire knowledge about machine learning on a modern statistical basis. They know mathematical-statistical approaches for solving pattern recognition problems and can apply them in exercises.

Prerequisite for participation There are no specific prerequisites.
Lecturer Zell
Literature / Other

Ch. Bishop: Pattern Recognition and Machine Learning, Springer-Verlag;
Skript in englischer Sprache

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, ML-CS



Module Number

INFO-4183
Module Title

Evolutionary Algorithms
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

To be announced.

Content

Outline and systematics of heuristic optimization methods, genetic algorithms, classifier systems, genetic programming, evolution strategies, multicriteria optimization, swarm algorithms. In the accompanying exercises, participants deepen the theory or solve simple optimization problems with the optimization system EvA2 and their own programs.

Objectives

The students know the theory and application of modern evolutionary algorithms (genetic algorithms, evolutionary strategies, genetic programming, swarm algorithms, etc.). They can select the optimal algorithms for the respective problem and solve optimisation problems with them.

Prerequisite for participation There are no specific prerequisites.
Lecturer Zell
Literature / Other

Skriptum zur Vorlesung

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4184
Module Title

Evolutionary Algorithms
Lecture Type(s)

Practical Course
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

To be announced.

Content

In teams of about three students, students become familiar with the evolutionary optimization system EvA2 and its optimization procedures, and then solve a real-world complex optimization problem in a team.

Objectives

Students have basic knowledge of evolutionary algorithms from the lecture and can apply these to a larger real-world problem. They master problem analysis, teamwork, time management, documentation and presentation techniques.

Prerequisite for participation INFO-4183 Evolutionary Algorithms
Lecturer Zell
Literature / Other

Wird in der Vorbesprechung ausgeteilt.

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4361
Module Title

Mobile Robots
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction German and English
Type of Exam

To be announced.

Content

The Mobile Robots module focuses in particular on wheel-driven robots and flying robots: introduction, mathematical foundations, kinematic modeling of wheel-driven mobile robots, sensors for mobile robots, mapping, localization, navigation of mobile robots, modeling of flying robots

Objectives

The students have acquired knowledge about mobile robots. They can describe the kinematics of mobile robots. They know algorithms for self-localisation, navigation, search and path planning. Furthermore, they know sensors for mobile robots and their properties and know their advantages and disadvantages for different tasks.

Prerequisite for participation There are no specific prerequisites.
Lecturer Zell
Literature / Other

Skriptum Mobile Roboter (Zell), weitere Lit. wird zu Beginn der Vorlesung bekanntgegeben

Last offered Sommersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS, ML-DIV



Module Number

INFO-4362
Module Title

Mobile Robots
Lecture Type(s)

Practical Course
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

To be announced.

Content

Using outdoor robots based on 1/10 model monster trucks with camera, sonar sensors and laser scanner, tasks such as wall tracking, control, following behavior, self-localization, map building or search algorithms are implemented on mobile robots. The last task usually has a competitive character between the teams.

Objectives

The students can work out problems of sensor technology, control, self-localisation and navigation of mobile robots independently in small groups. They have acquired competences in the areas of problem-solving behaviour, teamwork, time management, programming skills and presentation skills.

Prerequisite for participation INFO-4361 Mobile Robots
Lecturer Zell
Literature / Other

Literatur wird zu Beginn des Praktikums bekanntgegeben bzw. im Praktikum ausgeteilt

Last offered Wintersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS, ML-DIV



Module Number

INFO-4363
Module Title

Advanced Topics in Mobile Robots
Lecture Type(s)

Seminar
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

To be announced.

Content

The seminar covers annually changing advanced topics in mobile robotics, e.g. robot kinematics, modern probabilistic methods of navigation and self-localization, mapping, path planning with moving obstacles, robot formations, simultaneous localization and mapping (SLAM), visual self-localization, sensor fusion with different sensors. Sensors. In contrast to the similar proseminar mentioned above, the topics, algorithms and math/physics descriptions are more demanding and the treatment is more in-depth.

Objectives

Students can scientifically analyse a topic from the field of mobile robots, present it and elaborate on it in a paper.

Prerequisite for participation There are no specific prerequisites.
Lecturer Zell
Literature / Other

Literatur wird in der Vorbesprechung angegeben.

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDZ-SEM, ML-CS, ML-DIV



Module Number

INFO-4364
Module Title

Flying Robots
Lecture Type(s)

Practical Course
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

To be announced.

Content

Using 6+2 newly built flying robots (quadrocopters) with RGBD camera, onboard PC Odroid XU4 and autopilot hardware Pixhawk and a stationary IR tracking system, tasks such as simple flight control, autonomous hover, takeoff and landing, visual odometry, etc. are implemented. Most tasks will be performed first in a simulation system, then with real quadrocopters in an area separated by capture nets separated area of the large robotics laboratory.

Objectives

The students independently develop and implement algorithms for sensor data evaluation, flight control, self-localisation and visual odometry of flying robots in small groups. They acquire competences in the areas of problem-solving behaviour, teamwork, time management, programming skills and presentation skills.

Prerequisite for participation INFO-4361 Mobile Robots
Lecturer Zell
Literature / Other

Literatur wird zu Beginn des Praktikums bekanntgegeben bzw. im Praktikum ausgeteilt

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS, ML-DIV



Module Number

INFO-4365
Module Title

Deep Convolutional Neural Networks
Lecture Type(s)

Practical Course
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

To be announced.

Content

Using modern CUDA-based systems (PCs with Nvidia graphics processors as well as CUDA workstations with 4 GeForce Titan X graphics cards) and modern deep neural network training software, such as Caffe, CNTK or Torch, deep neural network machine learning problems for image classification, object recognition in images and object segmentation are investigated. Here, commonly available benchmark datasets such as NIST, Imageview, etc. are used, but also databases of RGB-D images (images with depth information, such as from the MS Kinect).

Objectives

The students can work out problems of programming, data preprocessing, structure selection of neural networks, training, validation and testing of deep neural networks independently in small groups. They have acquired competences in the areas of problem-solving behaviour, teamwork, time management, programming skills and presentation skills.

Prerequisite for participation There are no specific prerequisites.
Lecturer Zell
Literature / Other

Literatur wird zu Beginn des Praktikums bekanntgegeben bzw. im Praktikum ausgeteilt

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

INFO-4366
Module Title

Advanced Topics in Neural Networks
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction German and English
Type of Exam

To be announced.

Content

The seminar deals with yearly changing topics of artificial neural networks, e.g.
deep convolutional neural networks, recurrent neural networks, neural networks
for image classification or image segmentation, neural network for control, hybrid
classical - neural systems, etc.

Objectives

Advanced Topics in Neural Networks: Students can scientifically analyse a topic from the field of mobile robots, present it and elaborate on it in a paper.

Prerequisite for participation INF3154 Introduction to Neural Networks
Lecturer Zell
Literature / Other

Literatur wird in der Vorbesprechung angegeben.

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS, ML-DIV



Module Number

ML-4320
Module Title

Time Series
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

A time series is an extremely wide-spread type of empirical data: a (potentially
multivariate) set of observations that evolves over a univariate and thus ordered
index space—time. Examples include stock prices, inventory levels, sports
statistics, sensor readings in scientific equipment, cars and machinery, and many
more. Time series often require real-time processing, and can potentially be
infinitely long. But their univariate domain also allows for a crucial property
of the model: Markovianity, the ability to locally store all aspects of the model
necessary for inference in a time-local memory of fixed and finite size. This
course introduces a range of models and algorithms for efficient and flexible
inference in time series. Starting from famous concepts from the areas of signal
processing and control, we will move to recent and contemporary models
for structured, high-dimensional, non-linear and irregular time series. Alongside
data and models, efficient algorithms for approximate inference are a core
focus.
Apart from mathmatical derivations, the exercises put a focus on practical
programming. In particular, they contain implementations of some content of
the lectures.

Objectives

Students develop an understanding for key algorithmic and modelling challenges
in the analysis of, and practical inference with time-ordered processes and
data. They can implement and debug basic and advanced models for such data,
including for production-level, large-scale applications, and for areas demanding
high quality predictions, such as scientific analysis. Apart from mathmatical
derivations, the exercises put a focus on practical programming. In particular,
they contain implementations of some content of the lectures.

Prerequisite for participation There are no specific prerequisites.
Lecturer Hennig
Literature / Other

Literature will be listed at the beginning of the semester.

Last offered Sommersemester 2020
Planned for ---
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV



Module Number

ML-4101
Module Title

Mathematics of Machine Learning
Lecture Type(s)

Lecture, Tutorial
ECTS 9
Work load
- Contact time
- Self study
Work load:
270 h
Class time:
90 h / 6 SWS
Self study:
180 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction English
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

The lecture will repeat and introduce basic notions of mathematics used in machine learning
• Calculus: multivariate calculus (gradient and Hessian), Taylor expansion etc.
• Linear Algebra: eigenvectors, eigenvalues (including variational characterization), singular value decomposition and best low rank approximation, inverse and pseudo-inverse, norms, basic algorithms and their complexity (solving linear equations, matrix inversion, eigenvectors (power method)) etc.
• Probability: discrete and continuous probability measures (and mixed ones), basic notions, generation of random variables, conditional expectation and independence, law of large numbers and concentration inequalities for rates of convergence, central limit theorem etc.
• Statistics: parametric and non-parametric tests
• Optimization: Lagrangian and dual optimization problem, popular optimization techniques and their properties
• Optional: basic functional analysis and approximation theory, curse of dimensionality

Objectives

Students learn the mathematical foundations for the latter machine learning courses. In particular,
• they know multivariate calculus and linear algebra as needed in machine learning lectures
• they can apply probability and statistics and are able to prove basic properties
• they have an overview of existing optimization techniques and are able to reformulate equivalent constrained optimization problems

Prerequisite for participation INFM1010 Mathematics for Computer Science 1: Analysis,

INFM1020 Mathematics for Computer Science 2: Linear Algebra,

INFM2010 Mathematics for Computer Science 3: Advanced Topics
Lecturer Hein, Pons-Moll, von Luxburg
Literature / Other

The literature for this lecture will be provided at the beginning of the semester.

Last offered Wintersemester 2022
Planned for Wintersemester 2023
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV



Module Number

ML-4201
Module Title

Statistical Machine Learning
Lecture Type(s)

Lecture, Tutorial
ECTS 9
Work load
- Contact time
- Self study
Work load:
270 h
Class time:
90 h / 6 SWS
Self study:
180 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction English
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

The focus of this lecture is on algorithmic and theoretical aspects of statistical
machine learning. We will cover many of the standard algorithms, learn about
the general principles for building good machine learning algorithms, and analyze
their theoretical and statistical properties. The following topics will be
covered: Supervised machine learning, for example linear methods; regularization;
SVMs; kernel methods. Bayesian decision theory, loss functions,
Unsupervised learning problems, for example dimension reduction, kernel PCA,
multi-dimensional scaling, manifold methods; spectral clustering and spectral
graph theory.
Introduction to statistical learning theory: no free lunch theorem; generalization
bounds; VC dimension; universal consistency;
Evaluation and comparison of machine learning algorithms.
Advanced topics in statistical learning, for example low rank matrix completion,
compressed sensing, ranking, online learning.

Objectives

ML- 4201 Students get to know the most important classes of statistical machine learning
algorithms. They understand why certain algorithms work well and others
don’t. They can evaluate and compare the results of different learning algorithms.
They can model machine learning applications and get a feeling for
common pitfalls. They can judge machine learning algorithms from a theoretical
point of view.

Prerequisite for participation There are no specific prerequisites.
Lecturer Hein, von Luxburg
Literature / Other

The literature for this lecture will be provided at the beginning of the semester. / Students need to know the contents of the basic math classes, in particular
linear algebra and probability theory.

Last offered Sommersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV, ML-FOUND



Module Number

ML-4303
Module Title

Convex and Nonconvex Optimization
Lecture Type(s)

Lecture, Tutorial
ECTS 9
Work load
- Contact time
- Self study
Work load:
270 h
Class time:
90 h / 6 SWS
Self study:
180 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

Convex optimization problems arise quite naturally in many application areas
like signal processing, machine learning, image processing, communication and
networks and finance etc.
The course will give an introduction into convex analysis, the theory of convex
optimization such as duality theory, algorithms for solving convex optimization
problems such as interior point methods but also the basic methods in
general nonlinear unconstrained minimization, and recent first-order methods
in non-smooth convex optimization. We will also cover related non-convex problems
such as d.c. (difference of convex) programming, biconvex optimization
problems and hard combinatorial problems and their relaxations into convex
problems. While the emphasis is given on mathematical and algorithmic foundations,
several example applications together with their modeling as optimization
problems will be discussed.
The course requires a good background in linear algebra and multivariate calculus,
but no prior knowledge in optimization is required.

Objectives

Students learn the foundations of convex analysis and how to formulate and
transform optimization problems. After the lecture they know a variety of methods
for solving convex and non-convex optimization problems and have guidelines
which method to choose for which problem.

Prerequisite for participation There are no specific prerequisites.
Lecturer Hein
Literature / Other

The lecture does not follow a specific book. The literature for this lecture will
be provided at the beginning of the semester.

Last offered Sommersemester 2022
Planned for ---
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV



Module Number

ML-4302
Module Title

Statistical Learning Theory
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

Part 1: basic results in statistical learning theory:
• Statistical setup, estimation and approximation error, consistency
• Negative results: No free lunch theorem, slow rates of convergence
• Consistency of k nearest neighbor algorithms and partitioning algorithms
• Concentration inequalities
• Simple generalization bounds, for example with shattering coefficients and VC dimension
• Advanced generalization bounds, for example using Rademacher complexities, algorithmic stability, sample compression.
• Regularization and its consistency
Part 2: advanced results in statistical learning theory. This part of the lecture
changes, depending on the interests of the audience and the current state of
the art in the field and covers some of the recent results on learning theory. It
could cover topics like online learning, theory of unsupervised learning, theory
of deep learning, etc.

Objectives

Students get to know the standard tools and approaches in statistical learning
theory. They understand positive and negative results in learning theory, in
particular what are the fundamental limitations of machine learning, and which
properties are important to make a machine learning algorithm work.

Prerequisite for participation There are no specific prerequisites.
Lecturer von Luxburg
Literature / Other

The literature for this lecture will be provided at the beginning of the semester. / Students need to know the contents of the basic math classes, in particular
linear algebra and probability theory.

Last offered -
Planned for -
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV



Module Number

ML-4202
Module Title

Probabilistic Machine Learning (Probabilistic Inference and Learning)
Lecture Type(s)

Lecture, Tutorial
ECTS 9
Work load
- Contact time
- Self study
Work load:
270 h
Class time:
90 h / 6 SWS
Self study:
180 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction English
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

Probabilistic inference is a foundation of scientific reasoning, statistics, and
machine learning. The lecture course begins with a general introduction to basic
principles (rules of probability theory, graphical models), then covers the
probabilistic view on many standard settings, like supervised regression and
classification, and unsupervised dimensionality reduction and clustering. In a
parallel thread through the lecture, we will also encounter a number of popular
algorithms for inference in probabilistic models, including exact inference
in Gaussian models, sampling, and free-energy methods. At specific points,
connections and differences to non-probabilistic frameworks will be made.
Apart from mathmatical derivations, the exercises put a focus on practical
programming. In particular, they contain implementations of some content of
the lectures.

Objectives

Students gain an intuitive, as well as a mathematical and algorithmic understanding
of probabilistic reasoning. They acquire a mental toolbox of probabilistic
models for various problem classes, along with the algorithms required
for their concrete implementation. Over the course of the lecture, they also
become proficient in the fundamental concept of uncertainty, and the philosophical
challenges and pitfalls associated with it. They are empowered to build,
analyse, and use their own probabilistic models for concrete use cases.

Prerequisite for participation ML-4101 Mathematics of Machine Learning
Lecturer Hennig, Macke
Literature / Other

Literature will be listed at the beginning of the semester. / Standard undergraduate knowledge of mathematics is required, to the extent
that is provided, for example, by the course on Mathematics for Machine Learning
(ML 4101).

Last offered Sommersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV, ML-FOUND



Module Number

ML-4301
Module Title

Numerics of Machine Learning (Numerical Algorithms of Machine Learning)
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

The computational cost of machine learning is almost entirely caused by numerical
computations: Optimization for training and fitting of point estimates;
integration for marginalization and conditioning in probabilistic models; simulation,
i.e. the solution of differential equations for predictions of the future,
and linear algebra as the base case of all of the above. These tasks are often
solved with “black-box” tools, but those who want to build highly performant,
scalable, professional solutions need to know how these tools worn and adapt
them to the specific task. This course introduces basic and advanced tools for
the aforementioned tasks. It develops a holistic view of computation in the
context of, and within the conceptual framework of machine learning, moving
from classic concepts to recent developments.
Apart from mathmatical derivations, the exercises put a focus on practical
programming. In particular, they contain implementations of some content of
the lectures.

Objectives

Students develop both an intuitive and mathematical understanding of numerical
methods for optimization, integration, linear algebra, and the solution of
differential equation. They know how to adapt the tools to the challenges of
the task at hand, such as high dimensionality, stochasticity in computation,
numerical stability, non-convexity, efficient tuning of algorithmic parameters,
and uncertainty calibration for imprecise computation. Experience in the design
and use of numerical tools is a highly sought-after skill in industry, and
distinguishes the expert engineer from the amateur user.

Prerequisite for participation There are no specific prerequisites.
Lecturer Hennig
Literature / Other

Literature will be listed at the beginning of the semester. / Linear algebra is a core theme. Knowledge of probabilistic machine learning
is valuable for this course. Prior experience with numerical analysis is helpful
but not required. The practical parts use python and various recent python
libraries.

Last offered Wintersemester 2022
Planned for ---
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV



Module Number

ML-4310
Module Title

Data Mining and Probabilistic Reasoning
Lecture Type(s)

Lecture, Tutorial
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction English
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

The lecture gives an introduction into the basics of probability theory, statistics,
information theory, data (pre-)processing and indexing techniques, graph
representations and link analysis, classification, clustering and topic models,
probabilistic inference in graphical models.

Objectives

(1) The students acquire extensive knowledge in theory and application of
methods from the field of data science.
(2) The students acquire various data science techniques for conceptual thinking,
problem formalization and problem solving.
(3) The students are introduced to challenging research questions from the field
of data science.

Prerequisite for participation There are no specific prerequisites.
Lecturer Kasneci G
Literature / Other

Will be supplied (book chapters and papers in English)

Last offered Wintersemester 2022
Planned for ---
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV



Module Number

ML-4102
Module Title

Data Literacy
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction English
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

This course equips students with concepts and tools that should be familiar to anyone working with (large) data. Based on practical experiments and examples, frequently encountered pitfalls and problems are discussed alongside best practices. We encounter basic statistical notions and problems of bias, testing and experimental design. Foundational methods of machine learning and statistical data analysis are employed to employ these ideas in practice. We will also discuss best practices for scientific data presentation and documentation—how to make expressive figures and tables and perform reproducible experiments—and explore ethical and technical considerations in the context of fairness and transparency.
Apart from mathmatical derivations, the exercises put a focus on practical programming. In particular, they contain implementations of some content of the lectures.

Objectives

Students develop a sensitivity for common problems and misconceptions in empirical work with data. They understand the mathematical, epistemological, ethical, technical and social challenges surrounding the use of data, and know best practices to address them. They also collect a concrete box of software tools to collect, document, explore, visualize, and draw conclusions from structured, large, small, corrupted and expensive data.

Prerequisite for participation There are no specific prerequisites.
Lecturer Hennig
Literature / Other

Literatur / Literature: Wird zu Beginn des Semesters mitgeteilt. / Will be listed at the beginning of the semester.

Teilnahmevoraussetzungen / Course prerequisties: Grundlegende Kenntnisse in Mathematik und Programmierkenntnisse, wie bspw. durch einen B.Sc. in Informatik erworben. / Only basic math and coding skills as provided by the BSc Computer Science.

Last offered Wintersemester 2022
Planned for Wintersemester 2023
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV



Module Number

ML-4103
Module Title

Deep Learning (formerly: Deep Neural Networks; INFO-4182)
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction English
Type of Exam

Written exam

Content

Within the last decade, deep neural networks have emerged as an indispensable tool in many areas of artificial intelligence including computer vision, computer graphics, natural language processing, speech recognition and robotics. This course will introduce the (practical and theoretical) principles of deep neural
networks and give an overview over the most established training and regularization techniques. The lecture will further discuss the most important network variants, including convolutional neural networks, generative neural networks, recurrent neural networks and deep reinforcement learning. Furthermore, the course will give an overview over the most important architectures hourglass
networks, skip connections, dense connections, dilated convolutions, permutation invariant networks, siamese networks, etc.). In addition, applications from various fields will be presented throughout the course. The tutorials will deepen
the understanding of deep neural networks by implementing, training and applying them using modern deep learning frameworks.

Course Website: https://uni-tuebingen.de/de/175884

Objectives

Students gain an understanding of the theoretical and practical concepts of deep neural networks including optimization, inference, architectures and applications. After this course, students should be able to develop and train deep neural networks, reproduce research results and conduct original research in this area.

Prerequisite for participation There are no specific prerequisites.
Lecturer Geiger, Zell
Literature / Other

Related literature will be listed throughout the lecture.

Last offered Wintersemester 2022
Planned for Wintersemester 2024
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV, ML-FOUND



Module Number

ML-4340
Module Title

Self-Driving Cars
Lecture Type(s)

Lecture, Tutorial
ECTS 9
Work load
- Contact time
- Self study
Work load:
270 h
Class time:
90 h / 6 SWS
Self study:
180 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction English
Type of Exam

Written exam

Content

Within the last years, driverless cars have emerged as one of the major workhorses in the field of artificial intelligence. Given the large number of traffic fatalities, the limited mobility of elderly and handicapped people as well as the increasing problem of traffic jams and congestion, self-driving cars promise a solution to one of our societies most important problems: the future of mobility. However, making a car drive on its own in largely unconstrained
environments requires a set of algorithmic skills that rival human cognition, thus rendering the task very hard. This course will cover the most dominant paradigms of self-driving cars: modular pipeline-based approaches as well as deep-learning based end-to-end driving techniques. Topics include camera, lidar and radar-based perception, localization, navigation, path planning, vehicle
modeling/control, imitation learning and reinfocement learning. The tutorials will deepen the acquired knowledge through the implementation of several deep learning based approaches to perception and sensori-motor control in the context of autonomous driving. Towards this goal, we will build upon existing
simulation environments and established deep learning frameworks.

Course Website: https://uni-tuebingen.de/de/123611

Objectives

Students develop an understanding of the capabilities and limitations of stateof-the-art autonomous driving solutions. They gain a basic understanding of the entire system comprising perception, learning and vehicle control. In addition, they are able to implement and train simple models for sensori-motor control.

Prerequisite for participation There are no specific prerequisites.
Lecturer Geiger
Literature / Other

Related literature will be listed throughout the lecture.

Last offered Wintersemester 2022
Planned for Wintersemester 2025
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV



Module Number

ML-4380
Module Title

Advanced Topics in Machine Learning
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written exam (oral exam if the number of participants is small), successful participation in the exercise is considered a prerequisite for the exam, the lecturer decides on a bonus.

Content

The module provides an overview of advanced machine learning concepts and applications. Specific topics include the so-called core methods for extracting and analyzing nonlinear features from complex data, optimization methods for extremely large data sets, learning methods for sequential and structured data and security and confidentiality aspects of data analysis.

Objectives

Students have basic knowledge of machine learning on a modern statistical basis. They know mathematical statistical approaches for solving pattern recognition problems and can apply them in exercises. A further prerequisite is sound mathematical knowledge, especially in linear algebra, statistics and analysis.

Prerequisite for participation There are no specific prerequisites.
Lecturer Alle Dozenten
Literature / Other

J. Shawe-Taylor and N. Cristianini: Kernel Methods for Pattern Analysis. Cambridge University Press, 2004. Skript in englischer Sprache

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV



Module Number

ML-4350
Module Title

Reinforcement Learning
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction English
Type of Exam

Presentation and written report (or exam, will be determined)

Content

The lecture covers the whole range of reinforcement learning topics, from the basic formalism and theory to state-of-the-art algorithms.
The lecture is accompanied by exercises that help to deepen the mathematical understanding, as well as hands-on experience in implementing algorithms.

• Introduction to supervised learning and optimization
• Basics of Reinforcement Learning (RL) and Markov Decision Processes
• Dynamic programming, prediction and control
• Value function approximation
• Policy gradient
• Deep RL, control in continuous state-action domains
• Optimal control and model-based RL
• Advanced topics in RL

Objectives

(1) Students can phrase a problem in the reinforcement learning framework and
can select an appropriate algorithm for solving it.
(2) Students are able to implement a set of deep reinforcement learning algorithms
and analyse their behavior.
(3) Students can explain the challenges in reinforcement learning and assess
and characterize new reinforcement learning methods.

Prerequisite for participation There are no specific prerequisites.
Lecturer Martius
Literature / Other

Reinforcement learning by Sutton and Barto http://incompleteideas.net/
book/bookdraft2017nov5.pdf
Pattern Recognition and Machine Learning by C.M. Bishop, Chap. 3 and 5
Deep Learning by Goodfellow, Bengio and Courville https://www.
deeplearningbook.org / Recommended to attend basic Machine learning class before.

Last offered Wintersemester 2021
Planned for Sommersemester 2023
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV



Module Number

ML-4410
Module Title

Neural Data Analysis
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction English
Type of Exam

Written report and cumulative oral exam

Content

In recent years experimental methods to record brain activity have been revolutionized.
As the complexity of the data acquired in neuroscience increases,
neural data analysis becomes ever more important: The complex multidimensional
signals recorded with e.g. multielectrode arrays or two-photon imaging
can no longer be interpreted by eye, but rigorous data analytic techniques are
needed.
In this course we will cover a selection of topics related to the analysis of
different kinds of neural data based on concepts of machine learning: time
series analysis, spike sorting, spike triggered average/covariance, dimensionality
reduction techniques and information theory. The focus will be on applying
state-of-the-art concepts in hands-on data analysis of real data sets.

Objectives

(1) In this course students will acquire knowledge of basic and advanced techniques
necessary to analyze discrete (spike trains) and continuous (cellular voltage/
calcium signals, LFP, EEG) neural signals. (2) Students will implement
important techniques (Filtering, MoG, STA, etc) and evaluate them on artificial
and real data. (3) Students will learn how to work with real neural data
and cope with the challenges this brings about.

Prerequisite for participation There are no specific prerequisites.
Lecturer Berens
Literature / Other

Emery N Brown, Robert E Kass, und Partha P Mitra, „Multiple neural spike
train data analysis: state-of-the-art and future challenges“, Nat Neurosci 7, Nr.
5 (Mai 2004): 456-461.
Robert E. Kass, Valérie Ventura, und Emery N. Brown, „Statistical Issues in
the Analysis of Neuronal Data“, Journal of Neurophysiology 94, Nr. 1 (Juli 1,
2005): 8 -25.
Dayan and Abbott: Theoretical Neuroscience. MIT Press.
Rieke, Warland, Ruyter van Stevenik and Bialek: Spikes – Exploring the neural
code. MIT Press.

Last offered ---
Planned for ---
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV



Module Number

ML-4420
Module Title

Efficient Machine Learning in Hardware
Lecture Type(s)

Lecture
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction English
Type of Exam

Oral Test

Content

The recent breakthroughs in using deep neural networks for a large variety of
machine learning applications have been strongly influenced by the availability
of high performance computing platforms. In contrast to its biological origin,
however, high performance of artificial neural networks critically relies on much
higher energy demands. While the average energy consumption of the entire
human brain is comparable to that of a laptop computer (i.e. 20W), artificial
intelligence often resorts to large HPCs with several orders of magnitude higher
energy demand. This lecture will discuss this problem and show solutions on
how to build energy and resource efficient architectures for machine learning
in hardware. In this context, the following topics will be addressed:
• Hardware architectures for machine learning: GPU, FPGA, SIMD architectures,
domain-specific architectures, custom accelerators, in/near
memory computing, training vs. inference architectures
• Energy-efficient machine learning
• Optimized mapping of deep neural networks to hardware and pipelining
techniques
• Word length optimization (binary, ternary, integer, floating point)
• Scalable application specific architectures
• New switching devices to implement neural networks (Memristors, PCM)
• Neuromorphic computing

Objectives

The students gain in-depth knowledge about the challenges associated with
energy-efficient machine learning hardware and respective state-of-the-art solutions.
They can compare different hardware architectures regarding the tradeoff
between energy consumption, complexity, computational speed and the specificity
of their applicability. The students learn what kinds of hardware architectures
are used for machine learning, understand the reasons why a particular
architecture is suitable for a particular application, and can efficiently implement
machine learning algorithms in hardware.

Prerequisite for participation There are no specific prerequisites.
Lecturer Bringmann
Literature / Other

Will be announced in the first lecture / Knowledge about foundations in machine learning

Last offered Sommersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS, ML-DIV



Module Number

ML-4501
Module Title

Machine Learning Seminar
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Presentation and written report

Content

In this module we discuss advanced results and approaches in machine learning theory and application and current research results in the area of machine learning in general.

Please refer to the curse catalogue in alma to see which specific courses are offered in a respective semester.

Objectives

Students get to know about advanced results in machine learning theory and applications. They can judge for example whether an algorithm is well designed, both from an algorithmic and statistical point of view. They understand about the fundamental limitations of machine learning. They can reflect current research questions. Students will be able to acquire knowledge about current findings through comprehensive literature search. They will know the importance of current topics in the area of machine learning, and will be aware that there are still many open questions. Students will not only have improved their study and reading skills, but will also have enhanced their capability of working independently. The teaching method in this seminar aims at boosting the students’ confidence (oral presentation), and at enhancing their communication skills and enabling them to accept criticism (discussion session following their presentation. After this module they are well-prepared to write a master thesis in the area of machine learning.

Prerequisite for participation There are no specific prerequisites.
Lecturer Alle Dozenten
Literature / Other

Will be handed out in the course

Last offered Wintersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS, ML-DIV



Module Number

ML-4520
Module Title

Ethics and Philosophy of Machine Learning
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Presentation and written report

Content

The fact that machine learning algorithms will play a central role in the process
of scientific discovery challenges our traditional understanding of the scientific
process and raises fundamental questions about concepts of scientific discovery
and the role of the scientists. On the other hand machine learning has impact on
different aspects of our society which raises ethical concerns. In this course, we
will discuss the impact, changes, and challenges to the scientific methodology
as well as society in general by machine learning from the perspective of both
the philosophy and ethics.

Objectives

Students can reflect current research questions in the area of philosophy and
ethics of machine learning. Students will be able to acquire knowledge about
current findings through comprehensive literature search. They will know the
importance of current topics in the course-relevant areas, and will be aware
that there are still many open questions. Students will not only have improved
their study and reading skills, but will also have enhanced their capability of
working independently. The teaching method in this seminar aims at boosting
the students’ confidence (oral presentation), and at enhancing their communication
skills and enabling them to accept criticism (discussion session following
their presentation).

Prerequisite for participation There are no specific prerequisites.
Lecturer Ethics Lab
Literature / Other

Will be handed out in the course

Last offered Sommersemester 2022
Planned for currently not planned
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS, ML-DIV



Module Number

ML-4510
Module Title

Practical Machine Learning
Lecture Type(s)

Practical Course
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Oral presentation, written report, lab journal

Content

The practical course consists of finishing assigned tasks in small teams, autonomously
or under supervision. Study and exam performance are usually evaluated
based on active participation, a presentation of results and in written
reports.

Objectives

Students will gain practical experience in designing and programming methods
/ software /tools for ML. They will be able to use libraries and frameworks,
and will acquire knowledge or extend their knowledge of various programming
languages. By working together in groups, students obtain teamwork and collaboration
skills, and they will learn about project organization and presentation
techniques. Students will know about the strengths and weaknesses and about
the limitations of various methods for evaluating complex and high-dimensional
data, and will be able to describe and evaluate these methods.

Prerequisite for participation There are no specific prerequisites.
Lecturer Alle Dozenten
Literature / Other

-

Last offered -
Planned for -
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV



Module Number

ML-4998
Module Title

Research Project Machine Learning
Lecture Type(s)

Research Project
ECTS 9
Work load
- Contact time
- Self study
Work load:
270 h
Class time:
90 h / 6 SWS
Self study:
180 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Term Paper

Content

The research project serves to deepen theoretical and practical knowledge in a
specific field of machine learning. Students are working on a research project
with the main focus of the research group.

Objectives

Machine Learning research project: The students
• gain insight into scientific work,
• learn how to independently pursue a research question,
• learn to independently identify and compile scientific literature for the research question to be worked on,
• are able to work in a team in a scientific international environment,
• deepen their problem-solving skills.

Prerequisite for participation There are no specific prerequisites.
Lecturer Dozenten/Arbeitsgruppenleiter
Literature / Other

Scientific literature/publications relevant to the research topic to be addressed / Excellent academic grades in Master Machine Learning. There are only a few
research projects that are offered semester by semester. A written application,
including letter of motivation, CV and Transcript of Records should be sent to
the research group leader of the offered research project.

Last offered -
Planned for -
Assigned Study Areas ML-CS, ML-DIV



Module Number

MEDZ-4610
Module Title

Medical techniques
Lecture Type(s)

Lecture
ECTS 9
Work load
- Contact time
- Self study
Work load:
270 h
Class time:
90 h / 6 SWS
Self study:
180 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction English
Type of Exam

Written Test

Content

The following courses are to be attended:
- Bioimaging (3 ECTS): Image Correction, Functional MRI, Hyperpolarized MRI, Principles of Combined PET/MR Imaging, Basics of Image Reconstruction, Imaging and Metabolomics (MRI, NMR), Advanced Tracer development and production, MR Angiography, Research in Radiochemistry, Pharmacological Modelling
- Nanoanalytics/Interfaces I (3 ECTS): Introduction to statistical physics, soft matter and polymer physics, mechanics of cells and tissues, physics of the cytoskeleton, cellular forces, motor proteins, methods in nanobiophysics, high resolution microscopy techniques, micro- and nanofluidics, lab-on-a-chip technology.
- Implantology (3 ECTS): Vital implants: Tissue engineering, cell biology, biomaterials, reactor technology. Avital implants: Interface between tissue and man-made materials, signal acquisition and processing, biostability, biocompatibility, operational procedures, design and use in clinical trials.

The module handbook M.Sc. Biomedical Technologies for the current semester can be found at \url{http://www.medizin.uni-tuebingen.de/Studierende/Medizintechnik/Masterstudiengang+_Biomedical+Technologies_-port-10011-p-66480.html}.

Objectives

The exact qualification objectives can be found in the module handbook M.Sc. Biomedical Technologies. This can be found for the respective current semester at \url{http://www.medizin.uni tuebingen.de/Studierende/Medizintechnik/Masterstudiengang _Biomedical+Technologies_-port-10011-p-66480.html}.

Prerequisite for participation There are no specific prerequisites.
Lecturer Dozenten der Medizintechnik
Literature / Other

-

Last offered -
Planned for -
Assigned Study Areas INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDZ-MEDTECH, ML-CS



Module Number

MEDZ-4620
Module Title

Biorobotics
Lecture Type(s)

Lecture, Seminar
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written Test

Content

inhalt mz 4621

Objectives

goals mz 4620

Prerequisite for participation There are no specific prerequisites.
Lecturer Häufle
Literature / Other

-

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDZ-MEDTECH, MEDZ-SEM, ML-CS



Module Number

ML-4210
Module Title

Advanced Probabilistic Machine Learning Modeling and Applications
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written Test

Content

inhalt ml 4210

Objectives

goals ml 4210

Prerequisite for participation There are no specific prerequisites.
Lecturer De Bacco
Literature / Other

-

Last offered -
Planned for -
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV



Module Number

ML-4601
Module Title

Introduction to Game Theory with Application to Multi-Agent Systems
Lecture Type(s)

Lecture, Tutorial
ECTS 9
Work load
- Contact time
- Self study
Work load:
270 h
Class time:
90 h / 6 SWS
Self study:
180 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction English
Type of Exam

Written Test

Content

This module is about game theory and mechanism design, with an emphasis on
applications in different domains. The students study the essential concepts in
game theory such as equilibrium, belief, best-response dynamics, and the like.
Besides, they learn about strategic- and extensive form games, achieving equilibrium
in repeated games, games with incomplete and imperfect information.
Also, they obtain knowledge regarding other topics such as the Nash bargaining
solution, auctions, and computational models of human decision-making.
In brief, the students obtain broad knowledge about different branches of game
theory such as competitive-, cooperative-, and behavioral game theory, in addition
to studying detailed mathematical results, e.g., regarding the existence and
uniqueness of equilibrium in well-known scenarios. Besides theoretical foundations,
the students become familiar with the connection between game theory
and distributed control, and they gain experience in modeling and solving different
applied problems using game theory.

Objectives

After the lectures, the students have a broad and profound knowledge of essential
concepts of game theory. Therefore, they can identify the problems in
the applied domains that can be modeled based on game theory. The students
possess the ability to solve such problems by using the mathematical tools that
they have learned in this module. Besides, they have a high level of proficiency
in selecting, reading, analyzing, and criticizing scientific results, preparing technical
presentations, holding talks, and participating in discussions. Finally, the
students are independent learners and can expand their knowledge to advanced
levels in various topics of game theory.

Prerequisite for participation There are no specific prerequisites.
Lecturer Maghsudi
Literature / Other

• Mas-Colell and M.D. Whinston, and J.R. Green, Microeconomic Theory,
Oxford University Press, 1995
• Ozduglar, Game Theory with Engineering Application, MIT OpenCourseWare,
2009
• Fudenberg and D. Levine, The Theory of Learning in Games, MIT Press,
1998
• Fudenberg and J. Tirole, Game Theory, MIT Press, 1991
• Vijay, Auction Theory, Harvard University Press, 2008

Last offered Wintersemester 2022
Planned for Wintersemester 2023
Assigned Study Areas INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV



Module Number

ML-4502
Module Title

Machine learning methods for scientific discovery
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Oral presentation, written report

Content

In this seminar, we will discuss current and classical research papers which
describe machine learning methods for applications in the natural sciences.
From a methodological perspective, a particular focus will be on ‘simulationbased
inference approaches’, as these provide a bridge between data-driven
machine learning methods, and theory-driven scientific modelling, as well as on
latent-variable models for inferring dynamical systems from data.

Objectives

Students are able to read and reflect upon current research papers in this
research area. They can critically assess the contributions of such a paper. They
can present current research results to other students and researchers and can
lead research discussions. They can summarize and evaluate the results of a
paper in form of a written research report.

Prerequisite for participation There are no specific prerequisites.
Lecturer Macke
Literature / Other

Will be announced in the first meeting / Basic knowledge probabilistic machine learning

Last offered Wintersemester 2021
Planned for Sommersemester 2023
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS, ML-DIV



Module Number

ML-4360
Module Title

Computer Vision
Lecture Type(s)

Lecture, Tutorial
ECTS 9
Work load
- Contact time
- Self study
Work load:
270 h
Class time:
90 h / 6 SWS
Self study:
180 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction English
Type of Exam

Written exam

Content

The goal of computer vision is to compute geometric and semantic properties of the three-dimensional world from digital images. Problems in this field include reconstructing the 3D shape of an object, determining how things are moving and recognizing objects or scenes. This course will provide an
introduction to computer vision, with topics including image formation, camera models, camera calibration, feature detection and matching, motion estimation, geometry reconstruction, object detection and tracking, and scene understanding. Applications
include building 3D maps, creating virtual avatars, image search,
organizing photo collections, human computer interaction, video surveillance, self-driving cars, robotics, virtual and augmented reality, simulation, medical imaging, and mobile computer vision. Modern computer vision relies heavily on machine learning in particular deep learning and graphical models. This course therefore assumes prior knowledge of deep learning (e.g., deep learning lecture) and introduces the basic concepts of graphical models and structured prediction where needed.

Course Website: https://uni-tuebingen.de/de/203146

Objectives

Students gain an understanding of the theoretical and practical concepts of computer vision including image formation, camera models, feature detection, multiple view geometry, 3D
reconstruction, motion estimation, object recognition, scene understanding and structured prediction using deep neural networks
and graphical models. After this course, students should be able to understand
and apply the basic concepts of computer vision in practice, develop and train
computer vision models, reproduce research results and conduct original research
in this area.

Prerequisite for participation There are no specific prerequisites.
Lecturer Geiger
Literature / Other

Related literature will be listed throughout the lecture.

Last offered Sommersemester 2022
Planned for Sommersemester 2025
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV



Module Number

ML-4701
Module Title

An Introduction to Formal Epistemology and Ranking Theory in Particular
Lecture Type(s)

Lecture
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written Test

Content

inhalt ml 4701

Objectives

goals ml 4701

Prerequisite for participation There are no specific prerequisites.
Lecturer Spohn
Literature / Other

-

Last offered -
Planned for -
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV



Module Number

ML-4504
Module Title

Advanced Topics in Data Science and Analytics
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written Test

Content

inhalt ml 4505

Objectives

goals ml 4504

Prerequisite for participation There are no specific prerequisites.
Lecturer Kasneci G
Literature / Other

-

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS, ML-DIV



Module Number

ML-4430
Module Title

Machine Learning Approaches in Climate Science
Lecture Type(s)

Lecture
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written Test

Content

inahlt ml 4431

Objectives

goals ml 4430

Prerequisite for participation There are no specific prerequisites.
Lecturer Goswami
Literature / Other

-

Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV



Module Number

ML-4309
Module Title

Data Compression with and without Deep Probabilistic Models
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction English
Type of Exam

written exam

Content

This course covers lossless and lossy data compression from information theory to applications, and from established compression algorithms to novel machine-learning based methods. Research on data compression has made rapid progress in the last few years. Novel, machine-learning based methods are now beginning to significantly outperform even the best conventional compression methods, in particular for image and video compression.

We will discuss and prove information-theoretical foundations of compression (e.g., theoretical bounds on the bitrate, rate/distortion theory, and the source/channel separation theorem). Building on these concepts, we will then first discuss and analyze various established practical algorithms for data compression (e.g., Huffman Coding, Arithmetic Coding, Asymmetric Numeral Systems, and Bits-Back Coding). Finally we will cover the emerging field of machine-learning based data compression, discussing important methods like variational inference and deep probabilistic models such as variational autoencoders.

Detailed course schedule: https://robamler.github.io/teaching/compress23/
Enrollment (link fixed on 7 April 2023): https://ovidius.uni-tuebingen.de/ilias3/goto.php?target=crs_4116461

Objectives

On the theory side, you will learn information theoretical bounds for lossless and lossy compression, several algorithms for so-called entropy coding with their respective advantages and disadvantages, and the foundations of probabilistic machine learning, in particular scalable approximate Bayesian inference.

On the applied side, the tutorials will teach you how to implement entropy coding algorithms in real code and how to train various types of deep probabilistic machine learning models, integrate them into data compression algorithms, and evaluate their performance.

Prerequisite for participation There are no specific prerequisites.
Lecturer Bamler
Literature / Other

I will recommend some relevant literature in the first lecture. However, since this course covers an emerging field of research, there isn't any canonical reference yet that covers all discussed topics. Special-made and recently revised videos for all covered topics as well as lecture notes and solutions to the problem sets will be provided on the course website.

Students should already have a sound understanding of multivariate calculus and should be able to write simple programs in Python. Parallel attendance of the course "Probabilistic Machine Learning" will likely be helpful, but is not formally required.

Last offered Sommersemester 2022
Planned for Sommersemester 2023
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV



Module Number

BIOINF4382 (entspricht BIO-4382)
Module Title

Machine Learning for Single Cell Biology
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction English
Type of Exam

Oral Test

Content

Single-cell technologies in conjunction with machine learning approaches are
transforming the life sciences and the understanding of complex diseases like
cancer. This lecture provides an introduction into (1) the biological and medical
questions that can be uniquely addressed by such single-cell approaches, (2)
state-of-the-art single-cell technologies such as high dimensional mass-/flow
cytometry, multi-omic and/or spatial single-cell sequencing/imaging, and (3)
(un-)supervised machine learning and dynamic modeling approaches to address
afore questions on the basis of high dimensional single-cell data.

Objectives

• Overview state-of-the-art single-cell technologies
• Translation of biological/medical research questions into machine learning problems
• Unsupervised/Supervised/Weakly-supervised machine learning models for characterization of cellular composition of tissues and their association with health/disease states
• Dynamic models for cellular systems

Prerequisite for participation There are no specific prerequisites.
Lecturer Claassen
Literature / Other

Programmierkenntnisse Python

Last offered Wintersemester 2022
Planned for Wintersemester 2023
Assigned Study Areas BIO-BIO, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, ML-CS, ML-DIV



Module Number

BIOINF4103 (entspricht BIO-4103)
Module Title

Group Project Bioinformatics
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written elaboration

Content

The group project serves to deepen the knowledge in a specific area of bioinformatics
that have been introduced in either of the two lectures “Sequence Bioinformatics”
(BIO-4110) oder “Structure and Systems Bioinformatics” (BIO-
4120). Students work in small groups (4 students) and work on a project with
the thematic focus of either of the lectures. The project topics encompass algorithmic
problems, literature investigations, or application of tools to biological
data. The idea is to apply gained knowledge to real biological data/tasks. Student
have the opportunity to suggest a topic.

Objectives

Bioinformatics group project: The students
• gain insight into scientific work,
• learn how to independently pursue a research question,
• learn to independently identify and compile scientific literature for the research question to be worked on,
• are able to work in a team in a scientific international environment,
• deepen their problem-solving skills.

Prerequisite for participation There are no specific prerequisites.
Lecturer Dozenten/Arbeitsgruppenleiter
Literature / Other

Wissenschaftliche Literatur/Veröffentlichungen relevant für das zu bearbeitende Thema

Last offered Wintersemester 2022
Planned for Sommersemester 2024
Assigned Study Areas BIO-BIO, MEDZ-BIOMED, MEDZ-SEM



Module Number

BIOINF4104 (entspricht BIO-4104)
Module Title

Seminar Selected Topics in Bioinformatics
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Presentation and written report

Content

In this module we discuss advanced topic in the area of bioinformatics.

Information: In the summer term, the seminar "Statistical Population Genomics" (Instructor: Baumdicker) will be offered.

Objectives

Students get to know about advanced topics in bioinformatics theory and applications.
They can judge for example whether an algorithm is well designed, both
from an algorithmic and statistical point of view. They understand about the
fundamental limitations of the presented approached. They can reflect current
research questions. Students will be able to acquire knowledge about current
findings through comprehensive literature search. They will know the importance
of current topics in the area of bioinformatics, and will be aware that
there are still many open questions. Students will not only have improved their
study and reading skills, but will also have enhanced their capability of working
independently. The teaching method in this seminar aims at boosting the students’
confidence (oral presentation), and at enhancing their communication
skills and enabling them to accept criticism (discussion session following their
presentation.

Prerequisite for participation There are no specific prerequisites.
Lecturer Dozenten der Bioinformatik
Literature / Other

Will be handed out in the course

Last offered unknown
Planned for Sommersemester 2024
Assigned Study Areas BIO-BIO, BIO-SEM, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS



Module Number

ML-4506
Module Title

Machine Learning for Medical Image Analysis
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction English
Type of Exam

Oral presentation and graded participation in paper discussion

Content

The seminar starts with an introductory lecture to provide a compact overview of the research field (machine learning for medical image analysis), as well as a tutorial on critical analysis and presentation of research papers.
Throughout the remainder of the course, each student presents a paper from a collection of seminal work in the field. To foster engaging scientific exchange, each presented paper will have designated critics who are also tasked with studying the paper and preparing questions for its discussion.

Objectives

The learning objectives of this seminar consist of three parts: (1) the students will gain a solid understanding of key contributions to the field of machine learning for medical image analysis, (2) the students learn to critically read and analyse original research papers and judge their impact, and (3) the students will improve their scientific communication skills with an oral presentation and participation in discussions sessions.

Prerequisite for participation There are no specific prerequisites.
Lecturer Baumgartner, Koch
Literature / Other

Will be provided in the course

Last offered Wintersemester 2022
Planned for Wintersemester 2023
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS, ML-DIV



Module Number

ML-4507
Module Title

Autonomous Vision
Lecture Type(s)

Seminar, Proseminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Report, Review, Presentation, Participation

Content

Under module number ML-4507, the Autonomous Machine Vision Group offers regular proseminars and seminars. Currently, the following proseminars and seminars are offered:

Winter semester:
Proseminar/Seminar: 3D Vision
https://uni-tuebingen.de/de/215287

Summer semester:
Proseminar/Seminar: Autonomous Vision
https://uni-tuebingen.de/de/222456

Objectives

Students gain a deep unterstanding of a scientific topic. They learn to efficiently search, navigate and read relevant literature and to summarize a topic clearly in their own words in a written report. Moreover, students present their topic to an audience of students and researchers, and provide feedback to others in the form of reviews and discussions. During the seminar, students learn to put scientific research into context, practice critical thinking and identify advantages and problems of a studied scientific method.

Prerequisite for participation There are no specific prerequisites.
Lecturer Geiger
Literature / Other

-

Last offered Wintersemester 2022
Planned for Wintersemester 2024
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS, ML-DIV



Module Number

INFO-4390
Module Title

Visual Perception and Learning for Robotics
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

- Introduction to robot types and sensors
- Image formation
- Two-view geometry
- Deep learning basics
- Image motion and optical flow
- Keypoints and descriptors
- Camera motion estimation
- Probabilistic state estimation
- Visual simultaneous localization and mapping
- Visual-inertial odometry
- Event-based vision
- 3D object detection and tracking
- Learning-based planning and control from images

Objectives

(1) Students can phrase a robotic visual perception problem as algebraic, probabilistic state estimation or machine learning problem and can select an appropriate algorithm for solving it.

(2) Students are able to implement a set of robotic visual perception and learning algorithms and analyze their behavior.

(3) Students can explain the challenges in robotic visual perception and learning and assess and characterize new methods.

Prerequisite for participation There are no specific prerequisites.
Lecturer Stückler
Literature / Other

Recommended to attend deep learning course before. Basic programming skills in python required.

Lecture slides will be provided. Further literature will be announced in the lecture. Recommended textbooks:

- An Invitation to 3-D Vision by Yi Ma, Stefano Soatto, Jana Košecká, S. Shankar Sastry

- R. Szeliski's book on Computer Vision: Algorithms and Applications

- K. Murphy's book on Machine Learning: A Probabilistic Perspective

- Deep Learning by Goodfellow, Bengio and Courville
https://www.deeplearningbook.org

Last offered Wintersemester 2022
Planned for ---
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS, ML-DIV



Module Number

ML-4508
Module Title

Virtual Humans
Lecture Type(s)

Lecture, Tutorial
ECTS 9
Work load
- Contact time
- Self study
Work load:
270 h
Class time:
90 h / 6 SWS
Self study:
180 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction English
Type of Exam

Oral or Written (depending on the number of students)

Content

A virtual human is a digital representation of a real human. Virtual humans (VH) should look, move and eventually think like real humans. Building such VH is one of the long standing goals of Artificial Intelligence.
Learning them requires techniques and algorithms at the intersection of Machine Learning, Computer Vision and Computer Graphics. In this course, we will cover the key mathematical foundations and computational tools to learn VH from 3D scans, images and video of real humans. The course will cover classical representations of humans based on 3D meshes and textures, as well as modern ones where the appearance and behavior of virtual humans are encoded in neural networks.

Objectives

Understand the mathematical tools and algorithms to build VH from data. At the end of the course, students will be familiar with the state of the art in human motion and shape modeling, estimation of pose, shape and humans in clothing from images and video, as well as learning generative models of human motion conditioned on 3D scene geometry. Students should be able to apply the concepts in practice, develop and train models, reproduce research and conduct original research in this area.

Prerequisite for participation There are no specific prerequisites.
Lecturer Pons-Moll
Literature / Other

Related literature will be posted on the course website (https://virtualhumans.mpi-inf.mpg.de/DH22/). Knowledge of linear algebra, optimization and probability (e.g, mathematics for machine learning) and coding skills (Python).
Experience with deep learning (e.g, Deep Learning course).

Last offered Wintersemester 2022
Planned for Wintersemester 2023
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-HCI, MEDI-INFO, MEDI-MEDI, ML-CS, ML-DIV



Module Number

BIOINF4377 (entspricht BIO-4377)
Module Title

From Open Data to Open Science
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

english

Content

english

Objectives

english

Prerequisite for participation There are no specific prerequisites.
Lecturer Nahnsen
Literature / Other

Originalarbeiten und zusätzliche Literatur wird am Anfang der Vorlesung bekannt gegeben.

Last offered Wintersemester 2022
Planned for currently not planned
Assigned Study Areas BIO-SEM, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS



Module Number

BIOINF4250 (entspricht BIO-4250)
Module Title

Computational Single Cell Biology
Lecture Type(s)

Practical Course
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

A written report is to be submitted after the course. Performance during the course will also be integrated into the final grade.

Content

The basics of processing and automatically interpreting single-cell datasets are conveyed and applied on concrete examples in this practical course. The course tasks will be implemented in the scripting language Python and R. Specifically this course covers preprocessing, quality control of single-cell transcriptomic data and reconstruction of dynamic processes via RNA velocity analyses, trajectory inference and different dynamic models.

Objectives

(1)The students learn how to perform quality control and normalization of single-cell RNA sequencing data.
(2) They learn how to visualize high dimensional single-cell data.
(3) They learn how to perform RNA velocity analyses.
(4) They learn how to establish dynamic models from RNA velocity fields.

Prerequisite for participation There are no specific prerequisites.
Lecturer Claassen
Literature / Other

Will be supplied during the course.

Last offered Wintersemester 2022
Planned for currently not planned
Assigned Study Areas BIO-PRAK



Module Number

INFO-4501
Module Title

Digitalization & Innovation
Lecture Type(s)

Lecture
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction English
Type of Exam

Written exam

Content

In this course, students are provided with a broad overview on existing up-to-date theories (e.g. disruptive innovation), tools (e.g. data analytics, blockchain), methods (e.g. product management, agile project management) and technologies (e.g. 3D printing, robotics) in the context of digitization, digitalization and digital transformation along the entire value chain in industry. Focusing from strategy to implementation, the course is aimed to provide a diverse knowledge empowered by various practical examples, e.g. in case studies.
The aim of the course is to create an understanding for interdependencies, raise awareness and build up an understanding of the importance of digitalization motivating students to explore and further contribute to the digitalization process. For students attending other practical or theoretical courses addressing digitalization topics, this will be a fruitful and valuable complement and will enrich the students view on digitalization with practical examples from industry.
Concretely, the course will be addressing the three steps Innovation, Initiation, and Implementation. Means, in "Innovation" we will learn about the historic development and the driving forces of innovation, as well as the innovators dilemma. "Initiation" will address product and project management, both equally important to drive digitalization. In "Implementation" we will learn about the huge variety of tools and how they are embedded in industry. This ranges from software and technological advances, over additive manufacturing, operational networks, robotics, to how to ensure a green, safe, and responsible company.

Objectives

The students are able to explain the content presented and to analyze and evaluate the suitability of digitization technologies in industry. They can assess the applicability of methods such as disruptive innovation and agile project management. They know how to classify and evaluate the practical challenges of digitization in industry.

Prerequisite for participation There are no specific prerequisites.
Lecturer Wahl
Literature / Other

-

Last offered ---
Planned for Sommersemester 2023
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS



Module Number

ML-4440
Module Title

Trustworthy Machine Learning
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written exam (oral exam in case of a small number of participants)

Content

As machine learning technology gets applied to actual products and solutions, new challenges have emerged. Models unexpectedly fail to generalise well to small changes in the distribution; some models are found to utilise sensitive features that could treat certain demographic user groups unfairly; models tend to be confident on novel types of data; models cannot communicate the rationale behind their decisions effectively with the end users like medical staff to maximise the human-machine synergies. Collectively, we face a trustworthiness issue with the current machine learning technology. A large fraction of the machine learning research nowadays is dedicated to expanding the frontier of Trustworthy Machine Learning (TML). The course covers a theoretical and technical background for key topics in TML. We conduct a critical review of important classical and contemporary research papers on related topics and provide hands-on practicals to implement TML techniques.

Objectives

Students will be able to critically read, assess, and discuss research work in Trustworthy Machine Learning (TML). They will gain the technical background to implement basic TML techniques in a deep learning framework. They will be ready to conduct their own research in TML and make contributions to the research community.

Prerequisite for participation There are no specific prerequisites.
Lecturer Oh
Literature / Other

Prerequisites: Students should be familiar with Python and PyTorch coding. They should have basic knowledge of machine learning concepts: supervised learning, function fitting, generalisation gap, overfitting, and regularisation. Furthermore they should have basic knowledge of deep learning (CNNs and Transformers, their components, and empirical techniques for training and evaluating them), for instance by having passed ML-4103 (or equivalent). Basic maths skills (multivariate calculus, linear algebra, probability, statistics, and optimisation) are also required.

Last offered Wintersemester 2022
Planned for currently not planned
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV



Module Number

MEDZ-4710
Module Title

Data Privacy
Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Work load:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Oral presentation and written report

Content

This seminar covers current research topics in the field of data privacy and their applications. Topics include (but are not limited to) social network privacy, machine learning privacy, and biomedical data privacy.

Objectives

Students will learn, summarize, and present state-of-the-art scientific papers in data privacy. They can critically assess the contributions of a paper. They can present current research results to other students and researchers, and can lead research discussions. They can summarize and evaluate the results of research papers in form of a oral presentation and a written report.

Prerequisite for participation There are no specific prerequisites.
Lecturer Akgün
Literature / Other

Wird in der Vorbesprechung bekannt gegeben / Will be announced in a preparatory meeting at the start of the semester

Last offered Wintersemester 2022
Planned for currently not planned
Assigned Study Areas BIO-SEM, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS



Module Number

BIOINF4384 (entspricht BIO-4384)
Module Title

Machine Learning of Single-Cell Dynamics
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Work load:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency In the summer semester
Language of instruction English
Type of Exam

Excercises (not graded) must be passed. Written / oral exam (graded).

Content

Single-cell technologies have been used to reconstruct the dynamics of biological processes, such as signaling, differentiation and development. This course will review different types of technologies that have been developed and used to this end. At the core, this lecture will introduce and discuss different mathematical models for cellular dynamics, as well as classical and machine learning based system identification and model selection approaches to learn such models from single-cell data.

Objectives

(1) Overview of time resolved single-cell technologies
(2) Dynamic models for cellular systems
(3) Systems identification and model selection for dynamic models
(4) Machine learning for systems identification and model selection

Prerequisite for participation There are no specific prerequisites.
Lecturer Claassen
Literature / Other

Requirements: Programming skills in Python.

Last offered unknown
Planned for Sommersemester 2024
Assigned Study Areas BIO-BIO, ML-DIV



Module Number

BIOINF4383 (entspricht BIO-4383)
Module Title

Advanced Topics in Machine Learning for Single Cell Biology