ELE520: Mathematics of Data Science
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Course 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, spectral methods, tensor decomposition, graphical models, large-scale numerical linear algebra, matrix concentration inequalities, sparse recovery and compressed sensing, low-rank matrix recovery, shallow neural nets, etc. Both convex and nonconvex approaches will be discussed. We will focus on designing algorithms that are effective in both theory and practice.
Lectures
Tue, Thu 9:30 AM - 10:50 AM
Teaching Staffs
Instructor: Yuxin Chen, C330 Equad, yuxin dot chen at princeton dot edu
Teaching assistant:
Changxiao Cai, ccai at princeton dot edu
Yanxi Chen, yanxic at princeton dot edu