Einführung in Maschinelles Lernen/Introduction to Machine Learning (WS 2025/2026)

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

• Tom Mitchell (1997). Machine Learning. McGraw Hill. The classic introduction to ML from an Artificial Intelligence perspective.
• Chris Bishop (2007). Pattern Recognition and Machine Learning. Standard for ML from a pattern recognition perspective, statistical ML approaches.
• Peter Flach (2012). Machine Learning. The Art and Science of Algorithms that Make Sense of Data. One of most comprehensive machine learning textbooks covering rule learning as well as statistical machine learning.
• Ian Goodfellow et al. (2017). Deep Learning. An in-deepth coverage of deep learning.
• Uwe Schöning (2000): Logik für Informatiker
• Stuart Russell and Peter Norvig: Artificial Intelligence: A Modern Approach
• Andreas Lindholm et al.: Machine Learning: A First Course for Engineers and Scientists
• Sammut, C., & Webb, G. I. (2017). Encyclopedia of machine learning and data mining.
• Molnar, C. (2021). Interpretable Machine Learning.

Links/resources

• UCI Machine Learning Repository
• Fernández-Delgado et al. (2014). Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?, Journal of Machine Learning Research, 15, 3133-3181.
• Scikit-Learn
• PyTorch
• Kaggle
• Huggingface
• Popper ILP
• KNIME

Lecture Notes

• Foundations and History of Machine Learning

Symbolic Machine Learning

• Foundations of Concept Learning and Decision Trees
• Ensembles, Evaluation, and Genetic Algorithms
• Inductive Logic Programming (Pt. 1)
• Inductive Logic Programming (Pt. 2) and Other Approaches to Program Synthesis

Neural Networks

• Linear Decision Surfaces: Perceptron and Max Margins
• Non-Linear Functions: Multilayer Perceptrons (MLPs) and Kernels (SVMs)
• Instance Based Learning
• Foundations of Deep Learning Architectures (CNN, RNNs and LSTMs, Autoencoders)

• Excursus: Human Learning and Explainable Artificial Intelligence

Further Approaches to Machine Learning

• Probabilistic Machine Learning: Expectation Maximization
• Markov Chains and Hidden Markov Models
• Reinforcement Learning
• Discriminative vs. Generative Machine Learning

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] [WS 22/23] [WS 23/24] [WS 24/25]