ECE/SIOC 228 Machine Learning for Physical Applications


Teaching Assistants

Course Time and Location

Tuesday/Thursday, 2-3:20 pm, Warren Lecture Hall 2001

Course Structure

Part 0: Introduction & Fundamentals

Part 1: Modeling and Learning in Physical Systems

Part 2: Decision Making in Physical Systems

Part 3: Guest Lectures (ML for various physical applications) and Projects

Many thanks for the fun projects! Here are some example final projects presented at the poster event in Spring 2022! 

Predicting Wildfire in California

Crown of Thorns Starfish Detection

Hurricane Trajectory Detection using LSTM

Machine Learning for Breast Cancer Diagnosis

Textbook and References

There is no single text that provides adequate coverage of the concepts and the background material in to serve the needs of this course. There is no required textbook for the course. We will use my lecture notes and the following other references/lecture notes for material, each of which has a part of the relevant material for the course. 

Modeling and Learning in Physical Systems

Deep Learning, by Ian Goodfellow and Yoshua Bengio, and Aaron Courville

CS230 Deep Learning, Stanford, by Andrew Ng

Understanding Convolutions on Graphs, by Ameya Daigavane, Balaraman Ravindran, and Gaurav Aggarwal

Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations, by M. Raissi, P. Perdikaris, and G.E. Karniadakis

Decision Making in Physical Systems

An Introduction to Reinforcement Learning,  by Richard Sutton

Reinforcement Learning,  by David Silver

Deep Reinforcement Learning, by Sergey Levine