Machine Learning: Basic Course
Lecture and TutorialProf. Dr. Laurenz Wiskott
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Machine Learning: Basic CourseLecture and TutorialProf. Dr. Laurenz Wiskott |
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Lecture (2 SWS, 2 credit points): Tuesdays 12:00-13:30 o'clock
in the larger INI seminar room NB 3/57. First time 11.10.2011.
Tutorial (2 SWS, 4 credit points): Tuesdays 10:15-11:45 o'clock
in the larger INI seminar room NB 3/57. First time 18.10.2011.
Exam 1: Wednesday 2012-03-21 10:00-12:30 in HZO 90.
Exam 2: Wednesday 2012-09-19 11:00-13:30 in NB 2/99.
For the exams, only pen and ruler are permitted plus a
handwritten formulary (Formelsammlung) of one page DIN A4 (only
one side, not two sides written). Paper will be provided.
Description: This course covers a variety of unsupervised and supervised 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, support vector machines, optimization.
Language: The course will be given in English upon request.
Literature: For many topics a script will be available.
Prerequisites: Good command of linear algebra and calculus.
| # | date | topic |
| - | UNSUPERVISED LEARNING | |
| 1 | 11.10.2011 | Introductory remarks Principal component analysis I |
| 2 | 18.10.2011 | Principal component analysis II |
| 3 | 25.10.2011 | Principal component analysis III Fisher discriminant analysis |
| 4 | 08.11.2011 | Independent component analysis I |
| 5 | 15.11.2011 | Independent component analysis II |
| 6 | 22.11.2011 | Vector quantization |
| 7 | 29.11.2011 | Clustering |
| 8 | 06.12.2011 | Self Organizing Maps Slow Feature Analysis |
| 9 | 13.12.2011 | Bayesian inference |
| 10 | 20.12.2011 | Inference in Bayesian networks |
| 11 | 10.01.2012 | Inference in Gibbsian networks |
| 12 | 17.01.2012 | Learning in Bayesian networks |
| - | REINFORCEMENT LEARNING | |
| 13 | 24.01.2012 | Reinforcement Learning |
| 14 | 31.01.2012 | Summary and Review of All Lectures |