Machine Learning Theory

Description

How do we use mathematical thinking to design better machine learning methods? This course focuses on developing mathematical tools for answering these questions. This course will cover fundamental concepts and principled algorithms in machine learning. We have a special focus on modern large-scale non-linear models such as matrix factorization models and deep neural networks. In particular, we will cover concepts and phenomenon such as uniform convergence, double descent phenomenon, implicit regularization, and problems such as matrix completion, bandits, and online learning (and generally sequential decision making under uncertainty).

This course is cross listed with CS229M

Prerequisites

Linear algebra (MATH51 or CS 205L), probability theory (STATS 116, MATH151, or CS 109), and machine learning (CS 229 or STATS 315A)

A conferred Bachelor’s degree with an undergraduate GPA of 3.3 or better.

Course Availability

The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate education section.