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WS 2009/2010

Machine Learning: Basic Course

Lecture and Analytical Tutorial
Prof. Dr. Laurenz Wiskott and Jun. Prof. Dr. Christian Igel

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Analytical Tutorial: Tuesdays 12:15-13:45 o'clock in the upper INI seminar room ND 03/89,92.
Lecture: Tuesdays 14:15-15:45 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, basic knowledge of probability theory.


Schedule

# date topic lecturer
1 13.10.2009 Introductory remarks
Principal component analysis I
Wiskott
2 20.10.2009 Principal component analysis II Wiskott
3 27.10.2009 Principal component analysis III
Fisher discriminant analysis
Wiskott
4 03.11.2009 Independent component analysis I Wiskott
5 10.11.2009 Independent component analysis II Wiskott
6 17.11.2009 Vector quantization Wiskott
7 24.11.2009 Clustering Wiskott
8 01.12.2009 Slow Feature Analysis
Bayesian inference
Wiskott
9 08.12.2009 Inference in Bayesian networks Wiskott
10 15.12.2009 Inference in Gibbsian networks Wiskott
11 22.12.2009 Learning in Bayesian networks Wiskott
12 12.01.2010 Optimization Wiskott
13 19.01.2010 Markov-Random-Fields and Boltzmann Machines Igel
14 26.01.2010 dito Igel
15 02.02.2010 dito Igel

Exams

Exam 1: Mon, 22.02.2010, 10:00-12:30 o'clock in ND 3/99.

Exam 2: Wed, 22.09.2010, 10:00-12:30 o'clock in ND 3/99

Only pen, ruler, and 1 DIN A4 sheet of handwritten formulary (Formelsammlung) are permitted. Paper will be provided.


Laurenz Wiskott, http://www.neuroinformatik.ruhr-uni-bochum.de/PEOPLE/wiskott/