CS 281B / Stat 241B

Statistical Learning Theory

Spring 2001

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

People

Professor:
Michael Jordan (jordan@cs.berkeley.edu)
Office: 401 Evans, 2-8660; 731 Soda, 2-3806
Office hours: Mon 2:00-3:00 (731 Soda), Wed 2:00-3:00 (401 Evans)

TA:
Jenher Jeng (jenher@stat.berkeley.edu)
Office: 323 Evans
Office hours: Wed 4:00-5:00

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, the development of theoretical concepts to support such methods, and tools for model selection and model averaging.

Prerequisites:

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

Homework:

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