Lecture: Probabilistic Graphical Models


  • Lecturer(s):
  • Start: 18.10.2017
  • Dates: Wed. 12:15 - 13:45, LBH / HS III.a
  • Course number: MA-INF 4315
  • Curriculum: Master
  • Effort: 6 CP



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.


Please note that there is no exercise class on December 14.

There is no lecture on December 6 due to Dies Academicus!

Please note that there will be a lecture on November 29.

First lecture starts on October 18, 2017. See you there!

Please inscribe yourself into the mailinglist at: externhttps://lists.iai.uni-bonn.de/mailman/listinfo.cgi/vl-pgm.
In case you have a problem understanding something, questions related to  exercises/projects, please always feel free to write to the mailing list. This should be a place where you students can talk freely about the lecture, so please do not hesitate to ask and reply!


Additional Documents

Assignment Sheets

Exercise 1: ProbRefresh
Assignment sheet  (PDF document, 53 KB)
Exercise 2: BayesianNetworks
Assignment sheet  (PDF document, 66 KB)
Exercise 3: MarkovNetworks
Assignment sheet  (PDF document, 126 KB)
Exercise 4: VariableElimination
Assignment sheet  (PDF document, 105 KB)
Exercise 5: MsgPassing
Assignment sheet  (PDF document, 142 KB)
Exercise 6: JunctionTrees
Assignment sheet  (PDF document, 91 KB)
Exercise 7: Sampling
Assignment sheet  (PDF document, 64 KB)