CSE290O: Algorithmic Foundations of Convex Optimization

Focuses on some of the foundational aspects of convex and its relationship to modern machine learning. Discusses positive results--how can you solve convex optimization problems--and negative ones with statements like This family of problems is too hard to be solved in reasonable time. Course is divided into three parts, each exploring a different aspect of convex optimization: 1) algorithmic frameworks; 2) Oracle complexities; 3) the power of randomness. Through this course students are exposed to general concepts of convex geometry, learning theory, and rigorous proofs. (formerly CMPS 290O.)

Prerequisite(s): CSE 201 and CSE 242. Enrollment is restricted to computer engineering and computer science graduate students.

5 credits

Year	Fall	Winter	Spring	Summer

Comments

Formerly CMPS 290O

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