— Optimization for Big Data Analytics

Prerequisites & course information

This version has ended; see updated course, below. Course Description: Optimization algorithms for solving problems in statistics, machine learning, data analytics. Review computational tools for implementing optimization algorithms (gradient descent, stochastic gradient descent, coordinate descent, Newton’s method.) Learning Activities: Lecture 3 hour(s), Discussion 1 hour(s). Grade Mode: Letter. STA 209 — Optimization for Big Data Analytics (4 units) Course Description: Optimization algorithms for solving problems in statistics, machine learning, data analytics. Review computational tools for implementing optimization algorithms (gradient descent, stochastic gradient descent, coordinate descent, Newton’s method). Prerequisite(s): STA 200A ; STA 208 . Learning Activities: Lecture/Discussion 4 hour(s). Grade Mode: Letter. This course version is effective from, and including: Spring Quarter 2027. Learning Activities: Lecture 3 hour(s), Discussion 1 hour(s). Grade Mode: Letter. STA 209 — Optimization for Big Data Analytics (4 units) Course Description: Optimization algorithms for solving problems in statistics, machine learning, data analytics. Review computational tools for implementing optimization algorithms (gradient descent, stochastic gradient descent, coordinate descent, Newton’s method). Prerequisite(s): STA 200A ; STA 208 . Learning Activities: Lecture/Discussion 4 hour(s). Grade Mode: Letter. This course version is effective from, and including: Spring Quarter 2027.