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.

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

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

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

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