• acquisistion of know-how on modern graph-based methods for signal processing, communications, and machine learning
• capability of formulating and solving related engineering problems using graphical models
• mastery of english engineering terminology

• 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

Place: SEM 389 (room no. CG0118), Institute of Telecommunications.

Time: Tuesday, 1:00-3:00 p.m. (first class on March 5, 2019)

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

probability theory and random variables