Autumn 2021 STAT 37797

Course: STAT 37797

Title: Topics in Mathematical Data Science: Spectral Methods and Nonconvex Optimization

Instructor(s): Cong Ma

Teaching Assistant(s): 

Class Schedule: Sec 1: TR 3:30 PM-4:50 PM in Kent 101

Textbook(s): TBA

Description: This is a graduate level course covering various aspects of mathematical data science, particularly for large-scale problems. We will cover the mathematical foundations of several fundamental learning and inference problems, including clustering, ranking, sparse recovery and compressed sensing, low-rank matrix factorization, and so on. Both convex and nonconvex approaches (including spectral methods and iterative nonconvex methods) will be discussed. We will focus on designing algorithms that are effective in both theory and practice.