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WS 2010/2011 (310003)

Machine Learning: Basic Course

Lecture and Analytical Tutorial
Prof. Dr. Laurenz Wiskott

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Analytical Tutorial: Tuesdays 10:15-11:45 o'clock in the upper INI seminar room ND 03/89,92.
Lecture: Tuesdays 12:00-13:30 o'clock in the upper INI seminar room ND 03/89,92.

Description: This course covers a variety of unsupervised methods from machine learning such as principal component analysis, independent component analysis, vector quantization, clustering, self-organizing maps, growing neural gas, Bayesian theory and graphical models, deep-belief networks, and Markov random fields.

Language: The course will be given in English upon request.

Literature: For many topics a script will be available, other literature will be mentioned in the lecture.

Prerequisites: Good command of linear algebra and calculus.


Schedule

# date topic
- UNSUPERVISED LEARNING
1 12.10.2010 Introductory remarks
Principal component analysis I
2 19.10.2010 Principal component analysis II
3 26.10.2010 Principal component analysis III
Fisher discriminant analysis
4 02.11.2010 Independent component analysis I
5 09.11.2010 Vector quantization
6 16.11.2010 Independent component analysis II
7 23.11.2010 Clustering
8 30.11.2010 Self Organizing Maps
Slow Feature Analysis
9 07.12.2010 Bayesian inference
10 14.12.2010 Inference in Bayesian networks
11 21.12.2010 Inference in Gibbsian networks
12 11.01.2011 Learning in Bayesian networks
- SUPERVISED LEARNING
13 18.01.2011 Supervised Learning in Feedforward Networks
14 15.01.2011 Support Vector Machines
Nonlinear Expansion
15 01.02.2011 Kernel Trick
Optimization

Laurenz Wiskott, http://www.ini.rub.de/PEOPLE/wiskott/