Physics 361 Spring 2024 Reference Page

Machine Learning in Physics

A detailed introduction to the use of machine learning techniques in physics. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. A selection of physics topics that are particularly amenable to analysis using machine learning will be discussed. These might include processing collider data, classifying astronomical images, solving the Ising model, parameter estimation from physics data sets, learning physical probability distributions, finding string theory compactifications, and finding symbolic physical laws.

Prerequisites: MATH 234 and (PHYSICS 104, 202, 208, or 248), or graduate/professional standing

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