Lernende Systeme/Machine Learning (WS 2019/2020)

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

• Tom Mitchell: Machine Learning, McGraw Hill, 1997.
• Christopher Bishop: Pattern Recognition and Machine Learning, Springer, 2006.
• Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep Learning, MIT Press, 2016.

Links/resources

• 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.
• Christoph Molnar: Interpretable Machine Learning, 2018.
• Rapidminer
• KNIME

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

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