Machine Learning: Basic Course
Lecture and Analytical TutorialProf. Dr. Laurenz Wiskott
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Machine Learning: Basic CourseLecture and Analytical TutorialProf. 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.
| # | 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 |