Machine Learning in Physics

3 Credits

Introduction to machine learning methods and applications to physics, as well as to the statistical physics foundations of machine learning. Supervised and unsupervised learning, regression and classification techniques, dimensionality reduction, and feature engineering. Deep learning and neural network architectures, generative models, and reinforcement learning. Applications in diverse physics domains, physics-informed machine learning, and symbolic regression for discovering physical laws. Prerequisites: PHY 021 and MATH 205 Attribute/Distribution: Q