Intermediate Machine Learning

Undergraduate

S&DS 3650 is a second course in machine learning at the advanced undergraduate or beginning graduate level. The course assumes familiarity with the basic ideas and techniques in machine learning, for example as covered in S&DS 2650 . The course treats methods together with mathematical frameworks that provide intuition and justifications for how and when the methods work. Assignments give students hands-on experience with machine learning techniques, to build the skills needed to adapt approaches to new problems. Programming is central to the course, and is based on the Python programming language and Jupyter notebooks. 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. The Fall 2026 course features a new unit on diffusion models. Prerequisites: a background in probability and statistics at the level of S&DS 2420 ; familiarity with the core ideas from linear algebra, for example through MATH 2220 ; and computational skills at the level of S&DS 2650 or CPSC 2000 . QR HTBA