## ECE598: Interplay between Control and Machine Learning (Spring 2019)
## Course Information**Instructor:**Bin Hu (binhu7@illinois.edu)
**Term:**Spring 2019
**Office Hours:**Tu/Th 3-4pm, 145 CSL
**Lectures:**Tu/Th 12:30-1:50pm, Room 3020 ECEB
For a complete syllabus, see here.
## Course DescriptionAdvanced graduate course focuses on interplay between control and machine learning. The first half of the course focuses on tailoring control tools to study algorithms in large-scale machine learning. In the second half of the course, students will study how to combine reinforcement learning and model-based control methods for control design problems. We will cover some (or all) of the following topics: empirical risk minimization; first-order methods for large-scale machine learning; stochastic optimization; dissipation inequality; jump system theory; Lur'e-Postkinov type Lyapunov functions; integral quadratic constraints; KYP Lemma; graphical interpretations for optimization methods; adaptive control and ADAM; stable manifold theorem; Lyapunov measure; implicit bias of gradient descent on least square and logistic regression; robust control theory; algorithmic stability; policy gradient on linear quadratic regulator (LQR) problems; learning model predictive control for iterative tasks; zeroth-order optimization and evolutionary strategies; robust control via DK-iteration and IQC-synthesis; adversarial reinforcement learning; imitation learning.## Required MaterialsThere is no required textbook for the class. All course material will be presented in class and/or provided online as notes. Links for relevant papers will be listed in the resourse section of the course website. ## PrerequisitesMath 415, ECE 313, ECE 490 (or any similar course on optimization), and ECE 515 are required. ECE 534 is recommended, but not required. ## Grading60% regular homework sets (3 sets of homework in total); 40% written research report (detailed guidelines for the final projects will be posted in the resource section). |