# Graphical Models

 CS395T, Fall 2012 PAR 201, Mon & Wed 9:30 - 11:00

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 Overview Probabilistic Graphical Models provide compact and analytically useful representations of joint distributions over a large number of variables, using graphs. Each graph represents a family of distributions -- the nodes of the graph represent random variables, the edges encode independence assumptions, and weights over the edges and cliques specify a particular distribution within the family. There are two main classes of tasks within this framework: the first is to perform inference, given the graph structure and parameters; and the second is to learn the graph structure and parameters themselves from data. The course will cover these fundamentals of probabilistic graphical models, as well as related specialized topics, such as relational and template graphical models, dynamic graphical models, and causality. Grading Four problem sets (60 % of final grade), and a final project presentation (40 % of final grade). Textbooks & Papers Introduction to Graphical Models (in prep.) M. I. Jordan. (Notes to be handed in class) Graphical models, exponential families, and variational inference. M. J. Wainwright and M. I. Jordan. Foundations and Trends in Machine Learning, Vol. 1, Numbers 1--2, pp. 1--305, December 2008. Probabilistic Graphical Models: Principles and Techniques. D. Koller and N. Friedman. Discussion Board I will use https://piazza.com/utexas/fall2012/cs395t for class discussion. The Piazza system is highly catered to getting you help fast and efficiently from classmates, and myself. Rather than emailing questions to me, I encourage you to post your questions on Piazza. I will use Blackboard -- http://courses.utexas.edu -- to distribute course materials, and to post grades. Homework Policy Each student is expected to submit an individually written homework. When using information from papers, or other external sources, please cite this information. There will also be a final course project. This can either be a survey of existing work on a crisply outlined topic, or on original research. While a survey might seem easier, it might be worthwhile to pursue an original research project; the bar for such a research project will be lower. For the course project, you can either work individually, or in a team of two. Grading Policy Four Homeworks (60 %). Final Project (40 %): Initial Project Milestone (10%); due Oct 31 (tentatively); Final Project Presentation (30%); due final day of class (tentatively). The homeworks will be be due the beginning of class on the due date, unless otherwise specified. There will be two "free" late days, that you could use either all on one homework, or on two different homeworks. Otherwise, a homework will be worth 50% if one day late, and 0% if it is two or more days late. It is required to submit all homeworks even if after two days, if you do not want an incomplete grade. Class Particip. policy This is a graduate class; I expect students to participate actively in the class, as well as in the Piazza discussion site.