Convex Optimization and Applications in Machine Learning
COMPSCI 1280
Subject & Catalog Number
Course Information
Description
This course focuses on recognizing, formulating, and solving convex optimization problems. We will introduce basic convex analysis, discuss convex optimization theory, introduce algorithms for solving convex optimization problems, and touch on some advanced topics. We will explore all these in the context of machine learning applications, as almost every machine learning problem can be formulated as an optimization problem. The objective is to provide students with the theoretical training to recognize and formulate convex optimization problems and to equip them with the tools and methods to solve the problems of interest.
Course Notes
Course previously offered as CS 128.
Recommended Prep
Multivariable calculus (Math 21a), linear algebra (Math 21b/22a/25a/55a or equivalent), Python programming (CS 50, AM 10 or equivalent), prior exposure to formal mathematical reasoning at the level of Math 22a/b, Math 101, or CS 1200, and some basic understanding of probabilities.
Available for Harvard Cross Registration
