MATH 380 - Introduction to Optimization for Data Science (3)

This course provides an introduction to optimization, both in fundamental theory and algorithms, motivated by numerous modern applications from data science, machine learning, and artificial intelligence. It will cover linear programming, first-order algorithms for unconstrained or linear equality constrained optimization, such as gradient descent schemes, stochastic first-order algorithms for large-scale optimization, ADMM methods, as well as certain representative second-order algorithms, e.g., Newton and Quasi-Newton schemes.

Grading: Graded/Satisfactory Unsatisfactory/Audit
Course ID: 55231
Consent: No Special Consent Required
Components: Lecture
Attributes: Mathematics (GFR)
Prerequisite: MATH 221, MATH 251, MATH 300, STAT 332, and STAT 355 with a grade of ‘C’ or better.