Teaching

We are currently teaching two courses in Machine Learning at Yale. Introductory ML (S&DS 265) aims to be accessible to a wide range of students, from all disciplines, and is part of the Yale Data Science Certificate. Intermediate ML (S&DS 365) goes into more technical detail, and is required for the BS for majors in Statistics and Data Science.

Introductory ML

This course covers the key ideas and techniques in machine learning without the use of advanced mathematics. Basic methodology and relevant concepts are presented in lectures, including the intuition behind the methods. Examples come from a variety of sources including political speeches, archives of scientific articles, real estate listings, and natural images. Programming is central to the course and is based on the Python programming language.

Intermediate ML

This is a second course in machine learning at the advanced undergraduate or beginning graduate level. The course treats methods together with mathematical frameworks that provide intuition and justifications for how and when the methods work. Topics include: nonparametric regression and classification, kernel methods, risk bounds, nonparametric Bayesian approaches, graphical models, attention and language models, generative models, sparsity and manifolds, and reinforcement learning. Programming is central to the course, and is based on the Python programming language.