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Browse materialsMIT 9.520 · Spring 2003

Statistical Learning Theory and Applications

Subjects: Artificial Intelligence · Science · Statistics

Art: User:Looie496 created file, Santiago Ramon y…, Cajal cortex drawings, 2009 · Public domain

Instructors: Dr. Ryan Rifkin, Dr. Sayan Mukherjee, Prof. Tomaso Poggio, Alex Rakhlin
Term: Spring 2003
Level: Graduate
What's inside: 1 lecture note · 1 reading · 47 other files · 3 web pages
Materials open: MIT OCW

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9.520 · Spring 2006 · On MIT OCW ↗
9.520 · Spring 2003 · Study here (this version)

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Source: MIT OpenCourseWare 9.520 Statistical Learning Theory and Applications, Spring 2003, Dr. Ryan Rifkin, Dr. Sayan Mukherjee, Prof. Tomaso Poggio, Alex Rakhlin — original course page on ocw.mit.edu ↗. License: CC BY-NC-SA 4.0. Study order: MIT's own listing order, section by section. Taken from MIT's published course files, downloaded from ocw.mit.edu on September 23, 2026. How RedLectern is built.

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MIT OpenCourseWare, Dr. Ryan Rifkin, Dr. Sayan Mukherjee, Prof. Tomaso Poggio, Alex Rakhlin, 9.520 Statistical Learning Theory and Applications, Spring 2003.

License: CC BY-NC-SA 4.0 · Edition: Spring 2003 · Open the original course on OCW ↗

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Statistical Learning Theory and Applications

Before you start

What's here, and what isn't

Not included here

Materials by section

Lecture Notes

Assignments

Other materials from this course

Lecture 1: The Course at a Glance

Lecture 2: The Learning Problem in Perspective

Lecture 3: Regularization and Reproducing Kernel Hilbert Spaces

Lecture 4: Regression and Least-Squares Classification

Lecture 5: Support Vector Machines for Classification

Lecture 6: Generalization Bounds, Intro to Stability

Lecture 7: Stability of Tikhonov Regularization

Lecture 8: Consistency and Uniform Convergence over Function Classes

Lecture 9: Necessary and Sufficient Conditions for Uniform Convergence

Lecture 10: Bagging and Boosting

Lecture 11: Computer Vision, Object Detection

Lecture 12: Loose Ends

Lecture 13: Approximation Theory

Lecture 14: RKHS, Mercer Thm, Unbounded Domains, Frames and Wavelets

Lecture 15: Bioinformatics

Lecture 16: Text

Lecture 17: Regularization Networks

Lecture 18: Morphable Models for Video

Lecture 19: Leave-One-Out Approximations

Lecture 20: Bayesian Interpretations

Lecture 21: Multiclass Classification

Lecture 22: Stability and Glivenko-Cantelli Classes

Lecture 23: Symmetrization, Rademacher Averages

Lecture 24: Project Presentations

Lecture 25: Project Presentations