691GM: Graphical Models
Spring 2012
- Instructor: Benjamin M. Marlin
- Tentative Scheduling: TuTh 1:00PM-2:15PM, LGRC310
- Course Dates: Jan 23, 2012-May 1, 2012
- Course Text: Probabilistic Graphical Models by
Koller and
Friedman.
- 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.