EdusalsaDiscover Your Stanford

- Not Offered

3 units

Letter or Credit/No Credit

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). Prerequisites: linear algebra (MATH 51 or CS 205), probability theory (STATS 116, MATH 151 or CS 109), and machine learning (CS 229, STATS 229, or STATS 315A).

Already Have An Account? Log In

**Pranav Rajpurkar** is a PhD student in Computer Science at Stanford, working on Artificial Intelligence for Healthcare. He was previously a Stanford undergrad ('16).

**Brad Girardeau** got his B.S, M.S. degrees in computer science at Stanford ('16, '17). When not thinking about computer security, he can be found playing violin or running across the Golden Gate Bridge.

## Discussion

## To ask a question about a course and to share your perspective, signup or login