ELE538: Mathematics of High-Dimensional Data
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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
Mon, Wed 9:30 AM - 10:50 AM
Room: 101 Sherrerd hall
Teaching Staffs
Instructor: Yuxin Chen, B316 Equad, yuxin dot chen at princeton dot edu
Teaching assistant:
Cong Ma, 213 Sherrerd hall, congm at princeton dot edu
Changxiao Cai, C307 Engineering Quad, ccai at princeton dot edu