Patterns, Predictions, and Actions (Fall 2025)

Instructor: Ben Recht
Time: Tu/Th 3:30-5:00 PM
Location: 306 Soda Hall

GSIs: Jessica Dai, Brian Lee

This course will explore how patterns in data support predictions and consequential actions. Starting with the foundations of prediction, we look at the foundational optimization theory used to automate decision-making. We then turn to supervised learning, covering representation, optimization, and generalization as its key constituents. We will discuss datasets as benchmarks, examining their histories and scientific bases. We will then cover the related principles of statistical evaluation, drawing a through line from confidence intervals to AB testing to bandits to reinforcement learning. Throughout the course, we will draw upon connections to historical context, contemporary practice, and societal impact.

Required background: The prerequisites are previous coursework in linear algebra, multivariate calculus, probability and statistics. Some degree of mathematical maturity is also required. Numerical programming will be required for this course, though I am told anyone can do this with AI now. Let's find out.

Text: Patterns, Predictions, and Actions: A Story About Machine Learning by Moritz Hardt and Benjamin Recht. Available online. You can purchase a physical copy at the bookstore. You can also order online at Amazon or from Princeton University Press.

Blog: Ben will host a Class Live Blog on argmin.net.

For enrolled students: Detailed information regarding assignments, assessments, and logistics can be found on bcourses.

Problem sets

Schedule: (subject to change, especially November onward)