The next time this course will be taught is in the winter semester 2025/2026.
Description
The lecture covers the following topics, among others:
- Probability theory
- Regression and Gaussian processes
- Neural networks
- Classification
- Reinforcement learning
Information
Organizational information and changes will be communicated exclusively in the ILIAS course.
All documents relating to the lectures and exercises are available in the ILIAS course.
Prerequisites: Advanced Mathematics I+II, Computer Science (Programming), Statistics
Literature
- Ethem Alpaydin. Maschinelles Lernen. Oldenbourg Verlag, 2008.
- Jan Lunze. Künstliche Intelligenz für Ingenieure: Methoden zur Lösung ingenieurtechnischer Probleme mit Hilfe von Regeln, logischen Formeln und Bayesnetzen. De Gruytier Oldenbourg, 2016.
- Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer Verlag, 2006.
- Carl E. Rasmussen und Christopher K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006.
- Trevor Hastie, Robert Tibshirani und Jerome Friedman. The Elements of Statistical Learning. Springer Verlag, 2009.
Contact
Charlotte Stein
M.Sc.Research Assistant
- Profile page
- +49 711 685 66954
- Write e-mail
- Room 1.36
Matthias Ege
M.Sc.Research Assistant