18.465 Topics in Statistics: Statistical Learning Theory

Spring 2007

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

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.
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Staff

Instructor:
Prof. Dmitry Panchenko

Course Meeting Times

Lectures:
Three sessions / week
1 hour / session

Level

Graduate