• advanced methods and algorithmus in pattern recognition
• understanding of "state of the art" publications
• solving of practical and theortical problems
• knowledge of necessary basics to understand and implement new methods

Covered topics are for example: feature extraction, clustering methodologies, gaussian mixture models, different advanced classifiers, kernel methods, graphs, maps and grammars etc.

4,5 ECTS * 25 h = 112,5 h

30 h Lecture (VO)
82,5 h Exercises (UE):
- 32,5 h Pratical exercises
- 50 h  Theoretical exercises

Written and oral. Grading depends on the contributions during the whole term. Grade results from hand-ins and contributions during units.

(Mode inspired by: inverted class room)