Lernende Systeme/Machine Learning (WS 2013/2014)

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.

Recommended Reading / Links

Recommended textbook:

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

Relevant links:

• Machine Learning Eintrag bei aaai.org
• Machine Learning Abschnitt bei AI on the Web
• UCI Machine Learning Repository
• Statlog
• 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. Instance-based Learning [pdf]
8. Genetic Algorithms / Genetic Programming [pdf]
9. Bayesian Learning [pdf]
10. Kernel Methods, Support Vector Machines [pdf]
11. Hidden Markov Models
12. Reinforcement Learning [pdf]
13. Inductive Program Synthesis [pdf]
14. Learning in Graphical Models
15. Unsupervised Learning (Clusteranalysis) [pdf]
16. 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]