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.

2019S, VO, 2.0h, 3.0EC

Properties

  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VO Lecture

Aim of course

  • 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

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

Additional information

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

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

Lecturers

Institute

Course dates

DayTimeDateLocationDescription
Tue13:00 - 15:0005.03.2019 - 25.06.2019Sem 389 Vorlesung
Tue12:00 - 14:0004.06.2019Sem 389 Vorlesung
Graphical models in signal processing and communications - Single appointments
DayDateTimeLocationDescription
Tue05.03.201913:00 - 15:00Sem 389 Vorlesung
Tue12.03.201913:00 - 15:00Sem 389 Vorlesung
Tue19.03.201913:00 - 15:00Sem 389 Vorlesung
Tue26.03.201913:00 - 15:00Sem 389 Vorlesung
Tue02.04.201913:00 - 15:00Sem 389 Vorlesung
Tue09.04.201913:00 - 15:00Sem 389 Vorlesung
Tue30.04.201913:00 - 15:00Sem 389 Vorlesung
Tue07.05.201913:00 - 15:00Sem 389 Vorlesung
Tue14.05.201913:00 - 15:00Sem 389 Vorlesung
Tue21.05.201913:00 - 15:00Sem 389 Vorlesung
Tue28.05.201913:00 - 15:00Sem 389 Vorlesung
Tue04.06.201912:00 - 14:00Sem 389 Vorlesung
Tue18.06.201913:00 - 15:00Sem 389 Vorlesung
Tue25.06.201913:00 - 15:00Sem 389 Vorlesung

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