COMS 4771 Fall 2023

COMS 4771 is a graduate-level introduction to machine learning. The course covers basic statistical principles of supervised machine learning, as well as some common algorithmic paradigms.

Course information

Lecture: Tue/Thu 1:10pm–2:25pm (Section 1), 2:40pm–3:55pm (Section 2)
Lecture venue: TBD
Instructor: Daniel Hsu

Learning goals

Apply mathematical and statistical principles to understand and reason about machine learning problems and algorithms.
Apply algorithmic techniques to construct machine learning algorithms.

Enrollment

Enrollment and waitlists are managed by the CS department staff. Please do not contact the instructor about enrollment or waitlist issues.

Prerequisites

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

A more detailed list of topics is available here.

Online resources for course prerequisites:

Multivariable calculus: MIT Open Courseware
Linear algebra: Hefferon’s Linear Algebra
Probability: Grinstead and Snell’s Introduction to Probability (local copy with hyperref)
Programming in Python with NumPy: Python Tutorial, NumPy: the absolute basics for beginners