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