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MACHINE LEARNING IN PHYSICS

PHYSICS 361
Course Description

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.

Prerequisties

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

Satisfies

This course does not satisfy any prerequisites.

Credits

Not Reported

Offered

Not Reported

Grade Point Average
3.58

No change from Historical

Completion Rate
94.74%

No change from Historical

A Rate
63.16%

No change from Historical

Class Size
19

No change from Historical

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

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