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.
