Topics in Statistics: Statistical Learning Theory

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.)

Instructor(s)

MIT Course Number

18.465

As Taught In

Spring 2007

Level

Graduate

Cite This Course

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 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: Question_avt logo

Panchenko, Dmitry. 18.465 Topics in Statistics: Statistical Learning Theory, Spring 2007. (MIT OpenCourseWare: Massachusetts Institute of Technology), http://ocw.mit.edu/courses/mathematics/18-465-topics-in-statistics-statistical-learning-theory-spring-2007 (Accessed). License: Creative Commons BY-NC-SA


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