This course addresses the rising interest of data-driven methods and machine learning in the context of dynamical systems. These are systems whose state changes over time, such as autonomous vehicles or the temperature distribution within a room. The core topics include
- Dynamical systems basics (differential equations)
- Linear model identification
- Numerical optimization (for machine learning)
- Nonlinear model identification
- Feature engineering
- Model selection
- Control
This is a flipped-classroom course, where the content is provided via a series of videos available on YouTube. The course also covers extensive practical exercises using the Julia programming language. More details can be found on the accompanying GitHub page.
- Lehrende:r: Paul David Fährmann
- Lehrende:r: Sebastian Peitz
- Lehrende:r: Stefan Weigand