EdusalsaDiscover Your Stanford

- Not Offered

3 units

Letter or Credit/No Credit

Massive data sets are now common to many different fields of research and practice. Classical numerical linear algebra can be prohibitively costly in many modern problems. This course will explore the theory and practice of randomized matrix computation and optimization for large-scale problems to address challenges in modern massive data sets. Applications in machine learning, statistics, signal processing and data mining will be surveyed. Prerequisites: familiarity with linear algebra (ENGR 108 or equivalent), basic probability and statistics (EE 178 or equivalent), basic programming skills.

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