EE 270 - Large Scale Matrix Computation, Optimization and Learning

Announcements

  • Welcome to EE 270, Winter quarter 2019-2020.

Course description

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 ( EE 103 or equivalent), basic probability and statistics ( EE 178 or equivalent), basic programming skills.

Instructor: Mert Pilanci, pilanci@stanford.edu