Lernende Systeme/Machine Learning (WS 2018/2019)

General Information

• For a general course description please read the corresponding pages from the WIAI module guide.
• You find administrative information at UnivIS.
• Participants should sign up for the course in the virtual campus.
• This course can is elegible by students of the master in Surevy Statistics (MiSS). The module is an official import module for this degree.

Recommended Reading / Links

Recommended textbook:

• Tom Mitchell: Machine Learning, McGraw Hill, 1997.
• Christopher Bishop: Pattern Recognition and Machine Learning, Springer, 2006.

Relevant links/sources:

• Claude Sammut, Geoffrey I. Webb (Eds.): Encyclopedia of Machine Learning. Springer, 2010.
• Machine Learning Eintrag bei aaai.org
• Machine Learning Abschnitt bei AI on the Web
• UCI Machine Learning Repository
• Statlog
• Fernandez-Delgado et al. (2014). Do we Need Hundreds of Classiers to Solve Real World Classication Problems?, Journal of Machine Learning Research, 15, 3133-3181.
• Rapidminer

Lecture Notes

1. Basic Concepts of Machine Learning [pdf]
2. Foundations of Concept Learning [pdf]
3. Decision Trees, Avoiding Overfitting [pdf]
4. Perceptrons and Multilayer-Perceptrons [pdf]
5. Human Concept Learning [pdf]
6. Inductive Logic Programming [pdf]
7. Genetic Algorithms / Genetic Programming [pdf]
8. Instance-based Learning [pdf]
9. Bayesian Learning/Graphical Models [pdf]
10. Kernel Methods, Support Vector Machines [pdf]
11. Hidden Markov Models [pdf]
12. Reinforcement Learning [pdf]
13. Inductive Programming [pdf]
14. Unsupervised Learning (Clusteranalysis) [pdf]
15. Further Topics in and Applications of Machine Learning

Course Archive

[WS 04/05] [SS 05] [WS 05/06] [WS 06/07] [<link kogsys/teaching/archiv/ws0708/lernende_systeme/ - extern>WS 07/08</link>] [<link kogsys/teaching/archiv/ws0809/lernende_systeme/ - extern>WS 08/09</link>] [WS 09/10]  [WS 10/11]  [WS 11/12]  [ WS 12/13]  [WS 13/14] [WS 14/15] [WS 15/16] [WS16/17] [WS17/18]