EE 270 - Large Scale Matrix Computation, Optimization and LearningAnnouncements
Course descriptionMassive 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 |