The module provides an introduction to stochastic processes and stochastic control. It mainly focuses on Bayesian filtering theory and on stochastic differential equations.
Topics covered in the module are
- Basic facts about stochastic processes,
- Bayesian estimation and filtering theory,
- Introduction to stochastic differential equations, and Ito calculus as well as
- Applications in control and optimization.
Recommended prerequisites are basic knowledge about linear systems and control (e.g. System Theory I and II).
More detailed information on the course may be found on RWTHonline.
The teaching material is available on RWTHmoodle.