ELE522: Large-Scale Optimization for Data Science

Yuxin Chen, Princeton University, Fall 2019
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Course Description

This graduate-level course introduces optimization methods that are suitable for large-scale problems arising in data science and machine learning applications. We will first explore several algorithms that are efficient for both smooth and nonsmooth problems, including gradient methods, proximal methods, ADMM, quasi-Newton methods, as well as large-scale numerical linear algebra. We will then discuss the efficacy of these methods in concrete data science problems, under appropriate statistical models. Finally, we will introduce a global geometric analysis to characterize the nonconvex landscape of the empirical risks in several high-dimensional estimation and learning problems.


Teaching Staffs

  • Instructor: Yuxin Chen, B316 Equad, yuxin dot chen at princeton dot edu

  • Teaching assistant: Cong Ma, 220 Sherrerd hall, congm at princeton dot edu

  • Teaching assistant: Qingcan Wang, 218 Fine hall, qingcanw at princeton dot edu