CS 281B / Stat 241B

Statistical Learning Theory

Spring 2004

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[Syllabus] [Lectures] [Readings] [Homework] [Data and Software] [Announcements]

People

Professor:
Michael Jordan (jordan@cs.berkeley.edu)
Office: 401 Evans, 2-8660; 731 Soda, 2-3806
Office hours: Tues, 3-4 (401 Evans); Thurs 1-2 (731 Soda)

TA:
Chao Chen (chenchao@stat.berkeley.edu)
Office: 385 Evans
Office hours: Mon, 1-2; Weds, 1-2

Course Description:

This course will provide an introduction to advanced statistical and computational methods for the modeling of complex, multivariate data. The focus will be on nonparametric methods and the development of theoretical concepts to support such methods.

Prerequisites:

The prerequisite for this course is CS 281A / Stat 241A or a similar graduate-level probability or statistics course. Students will need to be familiar with Matlab, SPlus or a related matrix-oriented programming language.

Homework:

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