Optimization in Machine Learning, Spring 2018

A majority of machine learning algorithms minimize empirical risk by solving a convex or non-convex optimization. Structured predictors solve combinatorial optimizations, and their learning algorithms solve hybrid optimizations. And new approaches for stochastic optimization have become integral in modern deep learning methodology. Students who take this course will study the latest knowledge and foundational concepts on optimization in machine learning, including theoretical analyses of optimization-based learning algorithms, theoretical bounds of discrete optimization for structured prediction, and recent discoveries about non-convex optimization methods. Class meets Tuesday and Thursday from 12:30 PM to 1:45 PM in the New Classroom Building (NCB) 110A.