After successful completion of the course, students are able to apply methods from the areas of probabilistic graphical models and graph signal processing to practical engineering problems; this comprises the problem formulation, the analytical or numerical solution, and the qualitative and quantitative performance characterization.

• fundamentals of probability and graph theory
• applications
• types of graphical models (Bayesian networks, Markov random fields, factor graphs, ...)
• methods and algorithms for inference on graphs
• message passing, belief propagation
• variational methods
• Graph signal processing
• graph shift and graph Fourier transform
• graph filters
• graph signal recovery
• graph learning
• clustering

Conventional lectures on the blackboard supported by electronic media.

The lecture is held on Tuesday 1:00pm in Sem. 389 (CG0118).

First class: March 7, 2023 at 1pm

oral exam

Lecture notes are available.

Further References:

• Daphne Koller and Nir Friedman, "Probabilistic Graphical Models", MIT Press 2009
• Michael Jordan (Ed.), "Learning in Graphical Models", Kluwer 1998
• Christopher M. Bishop, "Pattern Recognition and Machine Learning", Springer 2006
• Petar Djuric and Cedric Richard (Eds.), "Cooperative and Graph Signal Processing", Elsevier 2018
• Antonio Ortega, "Introduction to Graph Signal Processing", Cambridge Univ. Press 2022

probability theory and random variables