CS 281A / Stat 241A

      Statistical Learning Theory      

Fall 2007

[Syllabus] [Homework] [Lectures] [Announcements] [Recitations] [Readings] [Data]


Michael Jordan (jordan@cs.berkeley.edu)
Offices: 731 Soda, 2-3806; 401 Evans, 2-8660
Office hours: Tuesday, 3:30-4 (401 Evans); Thursday, 3:30-4 (731 Soda); and by appointment

Percy Liang (pliang@cs.berkeley.edu)
Office: 711 Soda Hall
Office hours: Wednesday 3-5

Daniel Ting (dting@stat.berkeley.edu)
Office: 387 Evans Hall
Office hours: Monday 3-5

Course Description:

This course will provide a thorough grounding in probabilistic and computational methods for the statistical modeling of complex, multivariate data. The emphasis will be on the unifying framework provided by graphical models, a formalism that merges aspects of graph theory and probability theory.


The prerequisites for this course include previous coursework in linear algebra, multivariate calculus, and basic probability and statistics. Previous coursework in graph theory, information theory, optimization theory and statistical physics would be helpful but is not required. Students will need to be familiar with Matlab, Splus or a related matrix-oriented programming language.


M. I. Jordan, An Introduction to Probabilistic Graphical Models, in preparation. Copies of chapters will be made available.


There will be bi-weekly homework assignments, due one week after being passed out. Late homeworks will not be accepted.