Optional textbook: Koller and Friedman "Graphical Probability Models". We will also use chapters from "Introduction to Probabilistic Graphical Models" by Michael Jordan, of which we will hand out copies in class.

In addition, I will post my own course notes on the Handouts/Course notes page.

For extra reading and a web portal to the community of graphical models researchers go to http://www.auai.org the page of the Association for Uncertainty in AI. There is no similar portal for clustering but you can have a look to the many videolectures on clustering collected at videolectures.net

For graph theory a good book to have is West - "Introduction to graph theory". We will use only *very* small parts of it in this course. For algorithms and data structures, I recommend the classic CRLS, aka Cormen, Leiserson & Rivest "Introduction to Algorithms". This contains many of the tree algorithms that we'll be learning about.

Computing for graphical models16 December 2011, Royal Statistical Society, London. One day meeting of the Statistical Computing Section (organized by Joe Whittaker)

Additional research articles that will be discussed in class will be posted on the Handouts page.