Einführung in Maschinelles Lernen/Introduction to Machine Learning (WS 2022/2023)

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

• Peter Flach: The Art and Science of Algorithms that Make Sense of Data, CUP, 2012.
• 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, 2017.
• Machine Learning entry at aaai.org
• Machine Learning at 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.
• Python ML libraries
• 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. 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] [WS 20/21] [WS21/22]