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

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.

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.

• 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.

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

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.

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.

• 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.

Presentation and written report

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.

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.

Will be announced in the pre-lecture meeting.

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

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.

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.

Will be announced during the first lecture.

Elaboration and presentation of the internship tasks

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.

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.

Literatur wird zu Beginn des Praktikums bekanntgegeben.

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

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.

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.

• 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.

Elaboration and presentation of the internship tasks

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.

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.

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

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

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.

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.

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.

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

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

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.

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.

Final Project Presentation and Report

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.

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.

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

Final Project Presentation and Report

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

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

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

Final Project Presentation and Report

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

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

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

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

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.

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.

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.

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

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.

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.

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.

Presentation and written report

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.

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.

Wissenschaftliche Literatur/relevante Veröffentlichungen für das zu bearbeitende Forschungsthema.

Written elaboration and presentation based on it

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.

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.

Wissenschaftliche Literatur/Veröffentlichungen relevant für das zu bearbeitende Forschungsthema

Written elaboration and presentation based on it

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.

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.

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.

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

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.

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.

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.

To be announced.

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.

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.

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

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

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.

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.

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.

Presentation and written report

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.

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.

Will be handed out in the course

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

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

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

-

Oral Test

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

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

-

Written elaboration and presentation based on it

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.

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).

Originalarbeiten und zusätzliche Materialien werden im Seminar ausgegeben.

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.

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.

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.

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.

Will be announced at beginning of semester

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.

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.

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.

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

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.

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.

Vorlesungsfolien werden bereitgestellt

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

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).

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.

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

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

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

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.

• 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

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

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

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.

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

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

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).

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.

Klassische und aktuelle Forschungsliteratur zum Themengebiet.

Presentation

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.

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.

Wechselnde Original-Literatur aus dem Forschungsfeld der Datenbanksysteme

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

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).

TBD

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Programming project

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.

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.

Programming and bioinformatics literature

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

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.

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.

Scientific publications

Written or oral exam

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.

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.

Lecture notes and scientific publications

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

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.

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).

Scientific publications

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

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.

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).

Scientific publications

Written or oral exam

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.

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.

Lecture notes and scientific publications

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

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)).

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.

Vorlesungsfolien, zusätzliche Literatur wird bekanntgegeben

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

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

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.

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

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

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.

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

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

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.

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.

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

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

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.

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

Originalliteratur wird bekanntgegeben

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

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.

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.

wechselnd

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

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

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.

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

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.

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.

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.

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

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

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.

(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.

Will be supplied during the course.

Written elaboration and presentation based on it

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.

(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.

Materialien werden zur Verfügung gestellt.

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.

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.

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.

Originalarbeiten und zusätzliche Materialien werden ausgegeben.

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.

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.

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.

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

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.

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.

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.

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

Written elaboration and presentation based on it

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.

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.

Originalarbeiten und zusätzliche Materialien werden im Seminar ausgegeben.

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

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.

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.

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.

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

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.

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.

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

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

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.

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.

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

Term Paper

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.

Students acquire knowledge of the formal limits of computability

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

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.

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

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course-dependent

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)

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

je nach Seminarthema

Project, presentation and elaboration

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

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.

Entwicklungsumgebung wird zur Verfügung gestellt

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

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.

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.

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

Presentation and written report

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.

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.

Entwicklungsumgebung wird zur Verfügung gestellt, NVIDIA CUDA page

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

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.

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.

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

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

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.

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.

Vorlesungsfolien werden bereitgestellt / Grundkenntnisse im Bereich Computergrafik

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

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.

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.

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

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

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.

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.

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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.

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.

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.

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

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.

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.

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Graded internship attempts and an oral or written final exam

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.

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.

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

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.

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.

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

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.

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.

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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.

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.

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.

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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.

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.

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.

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

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.

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.

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

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

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.

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

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.

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.

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

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.

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.

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Oral presentation and written report

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

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.

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