I was the TA of the course “CSE 534A/ESE 513 Large-Scale Optimization” taught by Prof. Kamilov. I answer questions online and help with the arrangement of courses.
Large-scale optimization is an essential component of modern data science, artificial intelligence, and machine learning. This graduate-level course introduces optimization methods that are suitable for large-scale problems arising in these areas. We will learn several algorithms suitable for both smooth and nonsmooth optimization, including gradient methods, proximal methods, mirror descent, Nesterov’s acceleration, ADMM, quasi-Newton methods, stochastic optimization, variance reduction, as well as distributed optimization. Throughout the class, we will discuss the efficacy of these methods in concrete data science problems, under appropriate statistical models.