CS 281A / Stat 241A

      Statistical Learning Theory      

Spring 2014

[Syllabus] [Readings] [Data]


Michael Jordan (jordan@cs.berkeley.edu)
Offices: 427 Evans Hall
Office hours: Tues 11am-12pm, Thurs 11am-12pm

Hongwei Li (hwli@stat.berkeley.edu)
Office: 444 Evans Hall
Office hours: Tues 1pm-2pm, Fri 1pm-2pm

Xinghao Pan (xinghao@cs.berkeley.edu)
Office: 449 Soda Hall
Office hours: Mon 1pm-2pm, Weds 1pm-2pm

Virginia Smith (vsmith@cs.berkeley.edu)
Office: 411 Soda Hall
Office hours: Tues 2pm-3pm, Thurs 2pm-3pm

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.

The grade will be based on the homeworks and on a project that will be presented in a poster session at the end of the semester. The homeworks will count for 60% of the grade and the project will count for 40%.