COMS 4771 Fall 2025

This is the website for the course entitled “Machine Learning” for the Fall 2025 semester.

Basic course information

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

Description

The course description and list of prerequisites in Vergil are out-of-date.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

Learning goals

  • Familiarity with, and ability to reason about, core machine learning problems and methods
  • Ability to adapt machine learning methods for use in some specific applications

Prerequisites

List of prerequisites

There are several prerequisites for this course.

  • You must be fluent in multivariate calculus, linear algebra, and basic probability, all at the undergraduate level.
    • Calculus: MATH UN1201, MATH UN1202, MATH UN1205, APMA E2000 or equivalent
    • Linear algebra: COMS W3251, APMA E3101, APMA E2101, MATH UN2010 or equivalent
    • Probability: STAT UN1201, STAT GU4001, STAT GU4203, IEOR 3658 or equivalent
  • You must be comfortable with using (and writing programs in) Python to process and analyze data, and be familiar with basic algorithmic design and analysis.
    • Data structures: COMS W2132, COMS W3134, COMS W3136, COMS W3137 or equivalent
  • You must have mathematical maturity.
    • Some classes that help build mathematical maturity: COMS W3261, CSOR W4231 or equivalent

Rationale

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”.

Resources on prerequisites

Review notes for some of the prerequisites are available here.

Additional online resources for some course prerequisites are as follows.

More details to come.