PHYS 152Physics and Machine Learning
Review of select topics in statistical physics including information theory, entropy, coupled systems, phase transitions, and symmetry breaking. Introduction to multivariate algorithms, with an emphasis on their foundations in statistical physics and classical mechanics. Notebooks, data preparation, cross-validation, supervised and unsupervised learning. Practical considerations for training and optimizing neural networks and related tools. (Formerly offered as Neural Networks, Statistical Physics and Computing.)
Prerequisite(s): PHYS 105; and CSE 20 or ASTR 119 or PHYS 115 or prior programming experience with permission of instructor. Corequisite: PHYS 112.
- Discussion sections TBD - will be scheduled once class starts.
Find a section and add it to your scheduleLive seat counts, time-conflict checks and the walk from your previous class.
When it runs
Winter 2027Open Winter 2027
| Section | Type | When | Where | Instructor | Seats |
|---|---|---|---|---|---|
| 01 | Lecture | Mon Wed Fri 9:20am–10:25am | TBA | Staff | Open 0/25 |
Winter 2026Open Winter 2026
| Section | Type | When | Where | Instructor | Seats |
|---|---|---|---|---|---|
| 01 | Lecture | Mon Wed Fri 1:20pm–2:25pm | Soc Sci 1 145 | J. A. Nielsen | Open 14/20 |
Who teaches it
| Instructor | Rating | Difficulty | Would take again | Reviews |
|---|---|---|---|---|
| Jason Nielsen | 4.2 / 5 | 3.4 / 5 | 86% | 35 |
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