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


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. Inductive Logic Programming [pdf]
  7. Genetic Algorithms/Genetic Programming [pdf]
  8. Instance-based Learning [pdf]
  9. Bayesian Learning/Graphical Models [pdf]
  10. Human Concept Learning [pdf]
  11. Kernel Methods, Support Vector Machines [pdf]
  12. EM-Algorithm, Hidden Markov Models, LSTMs [pdf]
  13. Reinforcement Learning [pdf]
  14. Inductive Programming [pdf]
  15. Unsupervised Learning (Autoencoders, Kohonen nets)
  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]