Course Highlights
18.465 Topics in Statistics: Statistical Learning Theory
Spring 2007

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.
ACKNOWLEDGEMENT:
This course content is a redistribution of MIT Open Courses. Access to the course materials is free to all users.
This course content is a redistribution of MIT Open Courses. Access to the course materials is free to all users.