DATS-SHU-200: Optimization for Data Science and Machine Learning

This website is under construction. Course information is subject to change.

Course Objective: This course aims to give an introduction to optimization in data science and machine learning.

Instructor: Shuyang LING (sl3635@nyu.edu)

Lecture Time/Location: 3:45PM - 5:00PM on Mondays and Wednesdays, E202

Discussion/Recitation: 3:45PM - 5:00PM on Fridays, E202

Office hours: Room S750

Textbook: The course consists of various topics. I will provide lecture notes and reading materials throughout of the course. Here are several references:

• Optimization for Data Analysis, by Stephen J. Wright and Benjamin Recht, Cambridge Press, 2022.

• First-Order Methods in Optimization by Amir Beck, MOS-SIAM Series on Optimization, 2017.

• Convex Optimization: Algorithms and Complexity, by Sebastien Bubeck, Foundations and Trends in Optimization, 2015.

• Numerical Optimization by Nocedal and Wright. It is available free of charge on Springerlink if you are connected to the NYU wireless networks.

Grading policy:

• Homework

• Quiz

• Course Project

Late homework will not be accepted. Only a subset of problems will be graded.

Course schedule: The slides will be updated after each lecture. It is the first time this course is taught. The notes inevitably contain typos.