Machine learning in the context of scientific problems is an exciting emerging area of research, and often requires the development of new methods that can incorporate and exploit the inductive biases and structure needed for such problems. There are also now numerous examples of concepts in physics historically influencing machine learning methods development more broadly. This course will give an overview of different physics-inspired machine learning methods, and the connections between concepts in physics (numerical methods, dynamical systems, symmetries, conservation laws) and machine learning.

Repeat Rules

Course is not repeatable for credit.