9.520 -- Statistical Learning Theory and Applications
Course Description: Focuses on the problem of supervised and unsupervised 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 current research topics such as boosting, feature selection, active learning, ranking, and online learning. Examines applications in several areas: computer vision, computer graphics and bioinformatics. Final projects and hands-on applications and exercises, paralleling the rapidly increasing practical uses of the techniques described in the subject.

This class is at the Graduate level
Course Website: http://www.ai.mit.edu/projects/cbcl/courses/course9.520/
Instructor: T. Poggio, J. Bouvrie, R. Rifkin
Prerequisites: 6.867, 6.041, 18.06, or permission of instructor

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