COMS 4771 Fall 2025

Basic course information

• Lecture times: Tue/Thu 1:10pm--2:25pm (Section 1), 2:40pm--3:55pm (Section 2)
• Lecture venue: TBD
• Instructor: Daniel Hsu
• Office hours: TBD
• Teaching assistants: TBD

Enrollment

Please contact the CS Advising team for information about the waitlists. I am not managing the waitlists myself and will not be able to respond to questions about them.

Syllabus

The course description and list of prerequisites in Vergil are out-of-date.

COMS 4771 is a graduate-level introduction to machine learning. The course covers basic statistical principles and algorithmic paradigms of supervised machine learning.

There are several prerequisites for this course.

• You must be fluent in multivariate calculus, linear algebra, and basic probability, all at the undergraduate level.
• You must be comfortable with using, and writing programs in, Python to process and analyze data.
• You must be familiar with basic algorithmic design and analysis.
• You must have mathematical maturity.

Review notes for some of the prerequisites are available here.

Additional online resources for course prerequisites are as follows.

• Multivariable calculus
  • 18.02SC on MIT Open Courseware
• Linear algebra
  • Hefferon's Linear Algebra
  • COMS 3251 lecture notes
  • 3Blue1Brown videos
• Probability
  • Grinstead and Snell's Introduction to Probability (local copy)
  • Grinstead and Snell's book website
  • Stat 110 from Harvard
• Programming in Python with NumPy
  • Python Tutorial
  • NumPy: the absolute basics for beginners

More details to come.