Workload:
180 h
|
Module Number ML-4380 |
Module Title Advanced Topics in Machine Learning |
Lecture Type(s) Lecture |
|---|---|---|
| ECTS | 6 | |
|
Work load - Contact time - Self study |
Workload:
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. |
|
| Allocation of credits / grading |
Type of Class
Status
SWS
Credits
Type of Exam
Exam duration
Evaluation
Calculation
of Module (%)
Lecture
V
o
3
6.0
wt
90
g
100
|
|
| Prerequisite for participation | There are no specific prerequisites. | |
| Lecturer / Other | Alle Dozenten | |
| Literature | 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 | |