Lecture 1 (08/25/2026): Overview
Lecture 2 (08/27/2026): Background on State-Space Models for Control, Optimization, and Learning
Lecture Note
Lecture 3 (09/01/2026): Unifying the Analysis in Control and Optimization via Semidefinite Programs, Part I
Lecture 4 (09/03/2026): Unifying the Analysis in Control and Optimization via Semidefinite Programs, Part II
Slides
Lecture 5 (09/08/2026): Unconstrained Optimization for Smooth Strongly-Convex Functions
Lecture 6 (09/10/2026): Unconstrained Optimization for Smooth Strongly-Convex Functions (Continued)
Lecture 7 (09/15/2026): A Control Perspective on Certifiably Robust Neural Networks, Part I
Main Reference on LipSDP,
Lecture 8 (09/17/2026): A Control Perspective on Certifiably Robust Neural Networks, Part II
Main Reference (Read Section 3)
Lecture 9 (09/22/2026): A Control Perspective on Certifiably Robust Neural Networks, Part III
Lecture 10 (09/24/2026): Introduction to SDP-Based Design of Controllers and Lipschitz Networks
Lecture 11 (09/29/2026): Quadratic Constraints for Analyzing SGD
Lecture 12 (10/01/2026): Lur’e-Postnikov Lyapunov Functions and Nesterov's Acceleration Method
Lecture 13 (10/06/2026): Imitation Learning for Control
Lecture Note, Main Reference, Video for Main Reference
Lecture 14 (10/08/2026): Policy Optimization for Control
Lecture Note, Main Reference
Lecture 15 (10/13/2026): A Jump System Perspective on Temporal Difference Learning, Part I
Lecture 16 (10/15/2026): A Jump System Perspective on Temporal Difference Learning, Part II
Lecture 17 (10/20/2026): Online Learning for Control, Part I
ICML Tutorial from Prof. Elad Hazan and Karan Singh
Lecture 18 (10/22/2026): Online Learning for Control, Part II
Main Reference
Lecture 19 (10/27/2026): Robust Control Tools for Distributed Optimization
Main Reference: Pages 47-58 of Prof. Laurent Lessard's Slides, A Simplified Version of the Results
Lecture 20 (10/29/2026): Lure-Postnikov Lyapunov Functions
Lecture Note, Main Reference I, Main Reference II
Lecture 21 (11/03/2026): Statistical Learning Theory for System ID and Control, Part I
Main Reference: Prof. Stephen Tu's Tutorial slides
Lecture 22 (11/05/2026): Statistical Learning Theory for System ID and Control, Part II
Main Reference 1, Main Reference 2
Lecture 23 (11/10/2026): Diffusion Models and An Optimal Control Interpretation, Part I
Main Reference: CVPR 2023 Tutorial on Diffusion Models
Lecture 24 (11/12/2026): Diffusion Models and An Optimal Control Interpretation, Part II
Main Reference: Chapter 7 of Prof. Maxim Raginsky's SDE Book
Lecture 25 (11/17/2026): Neural Certificates for Control Systems, Part I
Lecture 26 (11/19/2026): Neural Certificates for Control Systems, Part II
Lecture 27 (12/01/2026): Intersection of Large Language Models and Feedback Control, Part I
A List of Useful Materials
Lecture 28 (12/03/2026): Intersection of Large Language Models and Feedback Control, Part II
Lecture 29 (12/08/2026): Summary and Future Directions