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18.465 Topics in Statistics: Statistical Learning Theory, Spring 2004

Image of Talagrand's convex-hull distance on the cube.

d2 represents Talagrand's convex-hull distance on the cube. (Image courtesy Prof. Dmitry Panchenko.)

Highlights of this Course

This course features lecture notes and assignments.

Course Description

The main goal of this course is to study the generalization ability of a number of popular machine learning algorithms such as boosting, support vector machines and neural networks. Topics include Vapnik-Chervonenkis theory, concentration inequalities in product spaces, and other elements of empirical process theory.
 

Staff

Instructor:
Prof. Dmitry Panchenko

Course Meeting Times

Lectures:
Three sessions / week
1 hour / session

Level

Graduate

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