d2 represents Talagrand's convex-hull distance on the cube. (Image by Prof. Dmitry Panchenko.)
Instructor(s)
Prof. Dmitry Panchenko
MIT Course Number
18.465
As Taught In
Spring 2007
Level
Graduate
Course Description
Course Features
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.
Other Versions
Other OCW Versions
This is a graduate-level subject in Statistics. The content varies year to year, according to the interests of the instructor and the students.Archived versions: 