389.111 Graphical models in signal processing and communications
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2021S, VO, 2.0h, 3.0EC

Properties

  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VO Lecture
  • Format: Distance Learning

Learning outcomes

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.

Subject of course

  • 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

Teaching methods

Remote teaching in the form of a flipped classroom:
* lecture videos for all topics are available at TUPeerTube
* before class: students look into the relevant videos and course materials
* in class (i.e., zoom meeting): discussion of the material, further examples, Q&A

Mode of examination

Oral

Additional information

All lectures are held on Tuesday 1:30pm via Zoom:

https://tuwien.zoom.us/j/96821760760?pwd=WXBWRzY5eWJya215aWQ4aXJELy9uUT09

Meeting ID: 968 2176 0760
Password: ZTkJbe9G

First class: March 2, 2021 at 1:30pm

Recordings of the lectures are available on the following TUpeerTube channel:

https://tube1.it.tuwien.ac.at/video-channels/graphicalmodels/videos

All other materials (slides, scans, etc.) are available on TISS.

Lecturers

Institute

Examination modalities

oral exam

Course registration

Not necessary

Curricula

Literature

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

Previous knowledge

probability theory and random variables

Language

English