Lernende Systeme/Machine Learning (WS 2020/2021)

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 Survey Statistics (MiSS). The module is an official import module for this degree.

Recommended textbooks

Links/resources

Lecture Notes

  1. Basic Concepts of Machine Learning [pdf]
  2. Foundations of Concept Learning [pdf]
  3. Decision Trees & Random Forests, Training and Evaluating Models [pdf]
  4. Perceptrons and Multilayer-Perceptrons [pdf]
  5. Deep Learning (CNNs, LSTNs, Autoencoder) [pdf]
  6. Human Concept Learning [pdf]
  7. Inductive Logic Programming [pdf]
  8. Genetic Algorithms/Genetic Programming [pdf]
  9. Instance-based Learning [pdf]
  10. Bayesian Learning/Graphical Models [pdf]
  11. Kernel Methods, Support Vector Machines [pdf]
  12. Hidden Markov Models [pdf]
  13. Reinforcement Learning [pdf]
  14. Inductive Programming [pdf]
  15. Unsupervised Learning (Kohonen nets) [pdf]
  16. Explaining blackbox models, Transparency and Interpretability, Fairness and unwanted biases, Ethical and responsible ML

Course Archive

[WS 04/05] [SS 05] [WS 05/06] [WS 06/07] [WS 07/08] [WS 08/09] [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] [WS18/19] [WS 19/20]