Probabilistic Graphical Models for Image Analysis

Dr. David Balduzzi, Dr. Brian McWilliams - Uni Basel, 19-30 August 2013

Jump to: Syllabus | Resources | Contact

Course Description

This course will focus on inference with statistical models for image analysis. We use a framework called probabilistic graphical models which include Bayesian Networks and Markov Random Fields. We apply the approach to traditional vision problems such as image denoising, as well as recent problems such as object recognition. The course covers amongst others the following topics:

Time and Place

Lectures 10-12, 13:00-15:00 Informatik, Seminarraum 205

Paper presentations: Should be in groups of 2 or 3 and should be 20 minutes in length. There will also be time for discussion after each presentation.

New (29 August 2013): Lecture 9 slides available.

New (19 August 2013): A list of interesting papers for you to choose from for the paper presentation is given in the following Google Doc

Lecture 1 slides available.


Day Lecture Topics Lecture Slides Additional Material
Aug 19 Introduction Lecture 1
Aug 20 Probabilistic modeling Lecture 2
Aug 21 Belief Networks Lecture 3
Aug 22 Markov Random Fields Lecture 4
Aug 23 Learning as Inference Lecture 5
Aug 26 Belief propagation/Junction Tree Lecture 6
Aug 27 Approximate inference (Variational) Lecture 7
Aug 28 Approximate inference (Sampling) Lecture 8
Aug 29 Conditional Random Fields Lecture 9



D. Barber. Bayesian Reasoning and Machine Learning. Cambridge University Press 2012.
The main course text. Brand new book which covers many topics in graphical models and machine learning. Available for free from here.

David J.C. Mackay. Information Theory, Inference and Learning Algorithms. Cambridge University Press, 2003.
Available for free from here.

C. Bishop. Pattern Recognition and Machine Learning. Springer 2007.
This is an excellent introduction to machine learning that covers most topics which will be treated in the lecture. Contains lots of exercises, some with exemplary solutions.

M. Wainwright and M.I. Jordan. Graphical models, exponential families and variational inference. Foundations and Trends in Machine Learning 2008.
Advanced treatment of graphical models and variational inference. Available free from here.

D. Koller and N. Friedman. Probabilistic Graphical Models: Principles and Techniques. The MIT Press 2009.
Covers Bayesian networks and undirected graphical models in great detail.


The official Matlab documentation is available online at the Mathworks website (also in printable form). If you have trouble accessing Matlab's built-in help function, you can use the online function reference on that page or use the command-line version (type help <function> at the prompt). There are several primers and tutorials on the web, a later edition of this one became the book Matlab Primer by T. Davis and K. Sigmon, CRC Press, 2005.


Dr. David Balduzzi
Dr. Brian McWilliams