Feedback, Learning, and Adaptation (Spring 2026)

Instructor: Ben Recht
Time: M 1-4 PM
Location: 310 Soda Hall

Description: This course investigates the foundations of information, learning, and adaptation in the design and study of complex feedback systems. We will study notions of robustness, adaptivity, and optimization in feedback systems, and their associated principles, advantages, and disadvantages. We will compare the trade-offs between rich hierarchical modeling of complex systems and purely data-driven models for analysis and planning. In tandem, we will explore the roles of forecasting, estimation, predictive modeling, and retraining in automated decision making for these systems. The goal of the course will be to draw together disparate perspectives and evaluation metrics across control, machine learning, and decision theory to make sense of how we interact with complex, dynamic processes.

Prerequisites: The technical content of this class sits at the intersection of machine learning and control theory. Introductory graduate background in both topics (e.g., CS281A and EE221A) is a required prerequisite.

Logistics: The course will meet once weekly and be run as a seminar. Students will be expected to scribe lecture notes, deliver a presentation on a paper related to the course material, and complete a project.

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.

References:

Schedule: (subject to change)