ECE 228 Machine Learning for Physical Applications
Description: This course provides an introduction to deep learning and its applications in physical systems and control. The course includes both the practical and theoretical aspects of the following topics: multi-layer perceptron, convolutional neural networks, recurrent neural networks, Transformers, graph neural networks, physics-informed neural networks, neural operators, and basics of deep reinforcement learning. It involves a group-based course project that provides a hands-on opportunity in conducting machine learning projects (for physical applications)!
ECE 171B Linear Control System Theory
Description: This course provides an introduction to systems and control in physical, biological, engineering, information, financial, and social sciences, etc. The focus is on the basic principles of feedback control systems and their use as tools for altering or inferring the dynamics of systems under uncertainty. The course includes both the practical and theoretical aspects of the following topics: Review of ODE and Linear Algebra, System Modeling, Linear Time-Invariant Systems, State Feedback Controller: Stability and Eigenvalue Placement, Output Feedback: Observability and State Estimation, Optimal Control: Linear Quadratic Regulator, Introduction to Reinforcement Learning.