Statistical Learning and Selected Applications (2V, 4.5 LP)
|SWS||Type||Course Form||CP (Effort)||Presence-Time / Self-Study|
|2||V||Lecture||4.5 CP||28 h||107 h|
|(2V)||4.5 CP||28 h||107 h|
|CP, Effort||4.5 CP = 135 h|
|Position of the semester||1 Sem. irreg.|
|Level|| Master (Advanced)|
|Area of study||[MAT-STO] Stochastics/Statistics/Financial Mathematics|
- Basic concepts of machine/statistical learning,
- Bayesian model choice,
- Local models,
- Random Forests, increased efficiency through combination of several experts, boosting,
- Support Vector Machines,
- Gaussian processes for regression and classification,
- Neural Networks,
- Identification of dynamic systems,
Selected application examples:
- Identification of vehicle systems for (load) prediction (e.g. predictive maintenance) and control (e.g. forcontrol units or driver assistance systems)
- Classification of working conditions based on easy to measure machine data,
- Data-driven driver and operator modeling, machine learning for autonomous driving,
- Reinforcement Learning for the control of traffic systems and in robotics.
- R. A. Berk: Statistical Learning from a Regression Perspective,
- C. Bishop: Pattern Recognition and Machine Learning,
- K. Murphy: Machine Learning: A Probabilistic Perspective,
- J. Franke: Lecture Notes on Nonparametric Statistics.
Further literature will be announced in the lecture.
Requirements for attendance (informal)
Classical regression analysis, e.g. from the module [MAT-60-12-M-4] Regression and Time Series Analysis.
- [MAT-10-1-M-2] Fundamentals of Mathematics (M, 28.0 LP)
- [MAT-14-14-M-3] Stochastic Methods (M, 9.0 LP)
Requirements for attendance (formal)
References to Course [MAT-62-21-K-7]
|[MAT-62-21-M-7]||Statistical Learning and Selected Applications||P: Obligatory||2V, 4.5 LP|