This is the website for the course entitled “Machine Learning” for the Fall 2025 semester.
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.
The course description and list of prerequisites in Vergil are out-of-date.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 introduces basic statistical principles and algorithmic paradigms of supervised machine learning.
There are several prerequisites for this course.
Machine learning is a confluence of ideas from many disciplines, including computer science, optimization, physics, and statistics. However, the common language of machine learning is rooted in the mathematical subjects of calculus, linear algebra, and probability. This language is used both to describe basic methods of machine learning, as well as to describe their underlying principles.
While many basic machine learning methods have been implemented in software packages, adapting these methods to new applications may require knowledge of their inner workings, and the ability to read, write, and reason about programs.
Despite the common language used in machine learning, the descriptions of the core methods, problems, and principles in textbooks, software manuals, and research articles are quite varied and possibly even contradictory. Machine learning is a relatively young field and is constantly changing. Mathematical maturity is essential to make sense of this “wild west”.
Review notes for some of the prerequisites are available here.
Additional online resources for some course prerequisites are as follows.