Optimization for AI

Catalog description

From fitting regression models to training neural networks, many problems in artificial intelligence can be cast as optimization problems. This course studies the fundamentals of optimization as they apply to artificial intelligence. Much of the focus will be on convex optimization, covering topics such as convex analysis, gradient descent for constrained and unconstrained problems, proximal gradient descent, variants of Newton’s method, and the Frank-Wolfe algorithm. The treatment will be at a fairly sophisticated level, giving sound justifications for the algorithms studied.