Courses Detail Information

ECE4850J – Optimization in Machine Learning


Xiaolin Huang

Credits: 3

Pre-requisites: MATH2140J/MATH4170J


Optimization in Machine Learning is a fundamental course for graduated students in Control Theory and Applications. In this course, optimization method, especially convex optimization, will be introduced, including theory and algorithm design. It will become the research basis for machine learning and other topics in both control and mathematics.

Course Topics:

In this course, the students will learn convex analysis foundation, optimization methods, and practice algorithms for typical machine learning models. Frontiers in this direction will be introduced as well. The main contents include: (a) convex optimization theory; (b) convex optimization algorithm design; (c) algorithm for machine learning models.