CSE 711: Convex Optimization in Robotics, Machine Learning, and Control
This page refers to the Fall 2012 offering of CSE 711 only. The information on this page does not necessarily apply to every offering of CSE 711.
Fall 2012
11689
Theory and applications of convex optimization to robotics, machine learning, and control
Many practical algorithms in robotics, machine learning, and control can be understood as solutions to convex optimization problems. Knowing the mathematical and optimization structure behind these algorithms will deepen our understanding of them. Moreover, by recognizing underlying convex structure in his or her own research problems, the student can take advantage of some of the latest mathematical and software tools in optimization theory. This two-part seminar aims to cover several basic yet fundamental topics in convex optimization with a focus on applications to robotics, machine learning, and control. Part 1 of the course (Fall 2012, lead by Robert Platt) will introduce fundamental topics in convex optimization, polynomial optimization, and convex relaxations. Each of these topics will be explored in the context of important applications to robotics, machine learning, and control. Part 2 of the seminar (Spring 2013, lead by Hung Ngo) will cover algorithms for solving convex optimization problems with a focus on the relationship between the algorithms and problem structure.
None presently required.
Ph.D.: This course does not fulfill core area or core course requirements.
M.S.: This course does not fulfill core area or core course requirements.
