IE 598 NH

Subject offerings of new and developing areas of knowledge in industrial engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: Approved for letter and S/U grading. May be repeated in the same or separate terms if topics vary.

Students are expected to have strong working knowledge iof linear algebra, real analysis, and probability theory. Some prior exposure to optimization and algorithms at a graduate level is preferred. The course will cover a variety of advanced topics in optimization theory, algorithms and applications in machine learning. The key aim of this course is to expose students to modern algorithmic developments in convex optimization (smooth, non-smooth, deterministic, stochastic, and online) and bring them near the frontier of current research in large-scale optimization and machine learning.