The subject is focused on the practical use of basic Bayesian modelling methods in the dynamically evolving machine learning theory. In particular, it studies the construction of appropriate models providing a description of real phenomena, as well as their subsequent use, e.g., for forecasting future evolution or learning about the hidden variables (true object position from noisy observations etc.).
The emphasis is put on understanding of explained principles and methods and their practical adoption. For this purpose, several real-world examples and applications will be presented to students, for instance, 2D/3D object tracking, radiation source term estimation, or separation in medical imaging. The students will try to solve some of them.
