Einführung in Maschinelles Lernen/Introduction to Machine Learning (WS 2023/2024)

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 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. 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

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