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.