Class 09: Sparsity Based Regularization

Instructor: Lorenzo Rosasco

Description

We introduce the problem of feature selection in supervised learning, focusing on sparsity based regularization techniques.

Slides

Slides for this lecture: PDF

Class Reference Material

L. Rosasco, T. Poggio, Machine Learning: a Regularization Approach, MIT-9.520 Lectures Notes, Manuscript, Dec. 2017

Chapter 6 - Sparsity, Low Rank and All That

Note: The course notes, in the form of the circulated book draft is the reference material for this class. Related and older material can be accessed through previous year offerings of the course.

Further Reading

• T. Hastie, R. Tibshirani and J. Friedman. The Elements of Statistical Learning, 2nd Ed., Springer, 2009.