Dr. David Balduzzi, Dr. Brian McWilliams  Uni Basel, 1930 August 2013
Jump to: Syllabus  Resources  ContactThis 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:
Lectures  1012, 13:0015:00 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 builtin help function, you can use the online function reference on that page or use the commandline 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.