After successful completion of the course, students are able to explain the most important topics of pattern recognition together with what makes them important, understand scientific standard papers and summarize them, solve small practical tasks with the methodology of the state of the art and to present the results, to critically discuss novel methodology.

Covered topics: feature extraction, introduction to structural methods, clustering methodologies, Gaussian mixture models, kernel methods, deep machine learning

This course is inspired by inverted class room model, each unit is dedicated to one topic.
For every unit there are several tasks to select and to prepare: summarize a video lecture, describe a chapter of a book, do literature research, do some practical exercises.
Topics are presented by students and discussed during the following lecture unit.
The LVA team are the moderators.

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

Up to three of the tasks of each unit can be selected and solved. The complete solution consists in a text and a few illustrative slides. Totally there are 15 point maximum to achieve in each unit. In addition there are Bonus points for good contributions to the discussion. On average every student will present twice during the whole lecture. This presentation will also be evaluated.