CS 281A / Stat 241A
Statistical Learning Theory
Fall 2007

[Syllabus]
[Homework]
[Lectures]
[Announcements]
[Recitations]
[Readings]
[Data]
People
Professor:
Michael Jordan
(jordan@cs.berkeley.edu)
Offices: 731 Soda, 23806; 401 Evans, 28660
Office hours: Tuesday, 3:304 (401 Evans); Thursday, 3:304 (731 Soda);
and by appointment
TA:
Percy Liang (pliang@cs.berkeley.edu)
Office: 711 Soda Hall
Office hours: Wednesday 35
TA:
Daniel Ting
(dting@stat.berkeley.edu)
Office: 387 Evans Hall
Office hours: Monday 35
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.
Prerequisites:
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
matrixoriented programming language.
Textbook:
M. I. Jordan, An Introduction to Probabilistic Graphical Models,
in preparation. Copies of chapters will be made available.
Homework:
There will be biweekly homework assignments, due one week after being
passed out. Late homeworks will not be accepted.