Machine Learning and Optimal Control
Organization
Credits: 5 ECTS
Term: Winter 23/24
Lecture & Exercise
Lecturer: Prof. Dr.-Ing. Timm Faulwasser
Schedule:
- Lecture: videos online
- QCD: Wednesday 12:15 - 13:45 (SRG1-2.010), starting 18.10.2023
- Exercise: Wednesdays 14:15 - 15:45 (CT-SR ZE 04), starting 25.10.2023
Language: English
Examination: Oral exam
Office hours: On demand
Enrollment: Please sign up for the course on LSF. You will then be enrolled in Moodle automatically.
Majors:
M.Sc. Electrical Engineering and Information Technology
M.Sc. Automation and Robotics: Module
Content
Machine Learning (ML) is a key technology of the 21st century. Applications for ML in technical and communicational systems are already ubiquitous. This lecture provides a system and control theory-based introduction for different aspects of ML. Starting with the fundamental distinction between unsupervised, supervised, and reinforcement learning, the following topics will be covered:
- Reinforcement Learning with its connections to Optimal Control (see Hamilton-Jacobi-Bellman equation and Dynamic Programming) and Model Predictive Control (MPC)
- Formulation of discrete and continuous state-spaces
- Formulation of supervised Deep Learning as an Optimal Control Problem
- Data-driven approaches of MPC for linear systems
The applications to these ML approaches are formally analyzed and tested with standard software (e.g. Matlab or Python).