9.520/6.860, Class 08

Class 08: Large Scale Learning by Sketching

Instructor: Lorenzo Rosasco


We discuss computational strategies for learning with large scale kernel methods, i.e. memory efficient when dealing with large datasets. We focus on subsampling methods that replace the empirical kernel matrix with a smaller matrix obtained by (column) subsampling.


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 4 - Regularization Networks

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