Course News:

Feb 13, 2013: The course number change for 691GM is compelte. The course will now be known as 688.
Dec 11, 2012: The Faculty Senate has approved 691GM as a regular course. It will eventually receive the new number CMPSCI 688.

Probabilistic Graphical Models

688 S2013

Instructor: Benjamin M. Marlin
Lectures: TuTh 2:30-3:45pm, CS 142
Course Dates: Jan 22, 2012-Apr 30, 2013
Course Website: The course website is hosted on the UMass Moodle portal. Registered students can access it at: moodle.umass.edu. Guest users can log in here using the password 691GMs2013 by clicking the "Login as Guest" button.
Course Text: Probabilistic Graphical Models by Koller and Friedman.
Course Syllabus: Download PDF
Course Description: Probabilistic graphical models are an intuitive visual language for describing the structure of joint probability distributions using graphs. They enable the compact representation and manipulation of exponentially large probability distributions, which allows them to efficiently manage the uncertainty and partial observability that commonly occur in real-world problems. As a result, graphical models have become invaluable tools in a wide range of areas from computer vision and sensor networks to natural language processing and computational biology. The aim of this course is to develop the knowledge and skills necessary to effectively design, implement and apply these models to solve real problems. The course will cover (a) Bayesian and Markov networks and their dynamic and relational extensions; (b) exact and approximate inference methods; (c) estimation of both the parameters and structure of graphical models. Although the course is listed as a seminar, it will be taught as a regular lecture course with programming assignments and exams. Students entering the class should have good programming skills and knowledge of algorithms. Undergraduate-level knowledge of probability and statistics is recommended. 3 credits.