Statistical Learning Theory and Applications, Spring 2006

Author:

Poggio, Tomaso

Subject:

Science and Technology

Institution Name:

M.I.T.

Collection:

MIT OpenCourseWare

Grade Level:

Post-secondary

Abstract:

This course focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. It develops basic tools such as Regularization including Support Vector Machines for regression and classification. It derives generalization bounds using both stability and VC theory. It also discusses topics such as boosting and feature selection and examines applications in several areas: Computer Vision, Computer Graphics, Text Classification and Bioinformatics. The final projects and hands-on applications and exercises are planned, paralleling the rapidly increasing practical uses of the techniques described in the subject.

Languages:

English

Material Type:

Activities and Labs, Full Course, Homework and Assignments, Lecture Notes, Syllabi

Media Format:

Text/HTML, Downloadable docs

Conditions of Use:

Creative Commons Attribution-Noncommercial-Share Alike 3.0