CS 281B / Stat 241B, Spring 2008:

Statistical Learning Theory


Office hours
Instructor Peter Bartlett bartlett at cs Tue, Wed 9:00-10:00, Soda 723.
David Rosenberg
drosen at stat Mon, 2:00-3:00, Evans 357; Thu, 2:00-3:00, Soda 551.

Lectures:  Soda 310. Tuesday/Thursday 12:30-2:00.

Course description

This course will provide an introduction to the design and theoretical analysis of prediction methods, focusing on statistical and computational aspects. It will cover methods such as kernel methods and boosting algorithms, and probabilistic and game theoretic formulations of prediction problems. We will examine questions about the guarantees we can prove about the performance of learning algorithms and the inherent difficulty of learning problems.
Prerequisites: CS281A/Stat241A, or advanced training in probability or statistics, at the level of Stat 205A or Stat 210A.
[More details]


The grade will be based 50% on homework, 40% on the final project, and 10% on lecture notes.

There will be roughly five homework assignments, approximately one every two weeks. Late homeworks will not be accepted. You are welcome to discuss homeworks with other students, but please work out and write up the solutions completely on your own, and specify in your solutions who you've discussed which problems with. Some of the problems have appeared in the literature. Please attempt them yourself, and if you need help, ask the instructor or GSI for assistance, rather than searching for someone else's solution. If you happen to have seen a problem before, please write up a solution on your own (and indicate that you've seen it before - it would also be helpful to point out where).

You will need to act as scribe for a small number of lectures, preparing a latex version of lecture notes (including figures if appropriate) and emailing it to the GSI within two weekdays of the lecture. These notes will be posted to the web site. Please use this latex template in preparing your lecture notes. Also, see the latex file of the notes for lecture 1.

There will be a final project. This can be in any area related to the topics of the course. You might implement an algorithm, run experiments on an algorithm for a particular application, try to extend an existing method or theoretical result, or do a combination of these. You will need to submit a brief written report and give a presentation in class in the last week of semester (a poster presentation or a talk, depending on the class size). It is OK to work on projects in groups of two (please email me an explanation if there's a good reason to work in a larger group). In all cases you will need to write the report individually.
Project proposals are due on March 31 (please send one or two plain text paragraphs in an email message to bartlett at cs).
Project reports are due on May 6.


See also the previous incarnation of the course: Spring 2006.



Lecture notes