CS 281A / Stat 241A
Statistical Learning Theory
Spring 2014

[Syllabus]
[Readings]
[Data]
People
Professor:
Michael Jordan
(jordan@cs.berkeley.edu)
Offices: 427 Evans Hall
Office hours: Tues 11am12pm, Thurs 11am12pm
TAs:
Hongwei Li
(hwli@stat.berkeley.edu)
Office: 444 Evans Hall
Office hours: Tues 1pm2pm, Fri 1pm2pm
Xinghao Pan (xinghao@cs.berkeley.edu)
Office: 449 Soda Hall
Office hours: Mon 1pm2pm, Weds 1pm2pm
Virginia Smith (vsmith@cs.berkeley.edu)
Office: 411 Soda Hall
Office hours: Tues 2pm3pm, Thurs 2pm3pm
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.
Evaluation:
There will be biweekly 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%.