Optimization Methods

Catalog description

The primary emphasis of the course will center around refining optimization methods, particularly within the realms of machine learning and artificial intelligence. Through this course, students will gain an understanding of foundational algorithms pertinent to continuous optimization. Beginning with the classical gradient descent algorithm in convex optimization, the course will progress towards advanced strategies tailored for non-convex scenarios. Topics covered will include fundamental theories, algorithmic intricacies, complexity considerations, and approximation techniques in nonlinear optimization. Prerequisite Course(s): Prerequisites: CSCI 353. Corequisites: CSCI 435. Classroom Hours - Laboratory and/or Studio Hours – Course Credits: 3-0-3