Probabilistic Graphical Models for Image Analysis

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:

• Traditional Supervised Learning: Logistic and Linear Regression, Naive Bayes.
• Directed and undirected Graphical Models: Markov Random Fields and Bayesian networks.
• Inference in Graphical Models: Sum-Product Algorithm, Graph-Cut, Belief Propagation, Variational Methods and Sampling.
• Applications of Graphical Models in vision: Conditional Random Fields and Structured Output Learning.

Time and Place

Office Hours

Exam

30 Minute oral exam in English.

Syllabus

Resources

References

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.

Matlab

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.

Contact

Dr. David Balduzzi
Dr. Brian McWilliams