Statistical Learning Theory and Applications, Spring 2003

Author:

Poggio, Tomaso

Subject:

Science and Technology

Institution Name:

M.I.T.

Collection:

MIT OpenCourseWare

Grade Level:

Post-secondary

Abstract:

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. Develops basic tools such as Regularization including Support Vector Machines for regression and classification. Derives generalization bounds using both stability and VC theory. Discusses topics such as boosting and feature selection. Examines applications in several areas: computer vision, computer graphics, text classification and bioinformatics. 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:

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