Vorlesung: Probabilistic Graphical Models


  • Dozent(en):
  • Beginn: 19.10.2016
  • Zeiten: Wed. 12:30 - 14:00, LBH / HS III.a
  • Veranstaltungsnummer: MA-INF 4315
  • Studiengang: Master
  • Aufwand: 6 CP
  • Prüfungen: written, date tbd



This course introduces probabilistic graphical models and their use in solving problems in computer vision and machine learning. Graphical models offer a probabilistic framework for modelling and making decisions in complex scenarios with limited and noisy data.  We will cover topics such as Markov and Bayesian networks, inference techniques and parameter learning.  The theory will be demonstrated in various vision applications.

No prior knowledge of statistics is required to follow the course.  Exercises will be both theory and programming (Matlab and Python) based and be completed in groups of two.


If you have conflicts for attending the exercises due to other classes, please email the TAs to arrange handing in assignments before the exercise session.  

First lecture starts on October 19, 2016!


Weitere Dokumente


Übung 1: ProbRefresh
Übungsblatt  (PDF-Dokument, 53 KB)
Übung 2: BayesianNetworks
Übungsblatt  (PDF-Dokument, 67 KB)
Übung 3: MarkovNetworks
Übungsblatt  (PDF-Dokument, 126 KB)
Übung 4: VarElim
Übungsblatt  (PDF-Dokument, 104 KB)
Übung 5: MsgPassing
Übungsblatt  (PDF-Dokument, 151 KB)
Übung 6: MsgPassing2
Übungsblatt  (PDF-Dokument, 92 KB)
Übung 7: JunctionSamplingI
Übungsblatt  (PDF-Dokument, 107 KB)