Statistical Learning Theory and Applications

Design of a system that will function the same way as a human visual system.

Designing and building a system that will function the same way as a human visual system, but without getting bored, and with a greater degree of accuracy. (Image courtesy of Poggio Laboratory, MIT Department of Brain and Cognitive Sciences.)

Instructor(s)

MIT Course Number

9.520

As Taught In

Spring 2006

Level

Graduate

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Course Features

Course Description

This course is for upper-level graduate students who are planning careers in computational neuroscience. 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, hands-on applications, and exercises are designed to illustrate the rapidly increasing practical uses of the techniques described throughout the course.

Poggio, Tomaso. 9.520 Statistical Learning Theory and Applications, Spring 2006. (MIT OpenCourseWare: Massachusetts Institute of Technology), http://ocw.mit.edu/courses/brain-and-cognitive-sciences/9-520-statistical-learning-theory-and-applications-spring-2006 (Accessed). License: Creative Commons BY-NC-SA


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