MIT OpenCourseWare

Browse materialsMIT 9.520-A · Spring 2001

Networks for Learning: Regression and Classification

Subjects: Science · Statistics

Art: Santiago Ramón y Cajal (1852—1934); uploaded to…, Fig retine, 1911 · Public domain

Instructors: Prof. Tomaso Poggio, Dr. Alessandro Verri
Term: Spring 2001
Level: Graduate
What's inside: 1 lecture note · 1 reading · 3 web pages
Materials open: MIT OCW

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About this course

Source: MIT OpenCourseWare 9.520-A Networks for Learning: Regression and Classification, Spring 2001, Prof. Tomaso Poggio, Dr. Alessandro Verri — 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, Prof. Tomaso Poggio, Dr. Alessandro Verri, 9.520-A Networks for Learning: Regression and Classification, Spring 2001.

License: CC BY-NC-SA 4.0 · Edition: Spring 2001 · Open the original course on OCW ↗

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Networks for Learning: Regression and Classification

Before you start

What's here, and what isn't

Lecture 1: The Course at a Glance

Lecture 2: The Learning Problem in Perspective

Lecture 3: Regularized Solutions

Lecture 4: Reproducing Kernel Hilbert Spaces

Lecture 5: Classic Approximation Schemes

Lecture 6: Nonparametric Techniques and Regularization Theory

Lecture 7: Ridge Approximation Techniques

Lecture 8: Regularization Networks and Beyond

Lecture 9: Applications to Finance

Lecture 10: Introduction to Statistical Learning Theory

Lecture 11: Consistency of the Empirical Risk Minimization Principle

Lecture 12: VC-Dimension and VC-bounds

Lecture 13: VC Theory for Regression and Structural Risk Minimization

Lecture 14: Support Vector Machines for Classification

Lecture 15: Project Discussion

Lecture 16: Support Vector Machines for Regression

Lecture 17: Current Topics of Research I: Kernel Engineering

Lecture 18: Applications to Computer Vision and Computer Graphics

Lecture 19: Neuroscience I

Lecture 20: Neuroscience II

Lecture 21: Current Topics of Research II: Approximation Error and Approximation Theory

Lecture 22: Current Topics of Research III: Theory and Implementation of Support Vector Machines

Lecture 23: Current Topics of Research IV: Feature Selection with Support Vector Machines and Bioinformatics Applications

Lecture 24: Current Topics of Research V: Bagging and Boosting

Lecture 25: Selected Topic: Wavelets and Frames

Lecture 26: Project Presentation