Spring 2027

Machine Learning in Physics

Introduction to machine learning in physics covering probability, optimization, and applications like collider data and image classification.

offering recorded3 credits
Recorded instructors · Spring 2027 Moritz Muenchmeyer

Summary

1 / 3

Recent recorded grades — Spring 2024: 3.78 GPA, 88.9% A/AB (n=18 letter grades); Spring 2025: 3.88 GPA, 94.4% A/AB (n=36 letter grades); Spring 2026: 3.74 GPA, 91.7% A/AB (n=48 letter grades).

What you’ll be able to do

  • Understand the conceptual foundations of machine learning and its application to physics problems. Be able to describe accurately and quantitatively the benefits of machine learning over other methods.

  • Master the techniques of supervised and unsupervised machine learning, and neural networks. Be able to apply this knowledge to identification and classification of physical events, and the analysis of data.

  • Become familiar with the programming techniques and common tools used in machine learning. Learn how to construct a working program and to recognize and solve problems that may arise in a large piece of code.

  • Grasp the big picture behind a project that involves machine learning. Be able to articulate the goals, and the project, and where it fits into the overall scientific context.

  • Understand how novel machine learning developments from the last years can be used to solve physics problems that were previously intractable.

Source: UW Guide

Grade history

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average GPA
letter grades
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AB
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All recorded terms · compare terms & instructors

Prerequisites

Course map

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

PHYSICS 361

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027
Historical instructors & teaching patterns

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 750

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
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Student experience

the class

Recent recorded grades — Spring 2024: 3.78 GPA, 88.9% A/AB (n=18 letter grades); Spring 2025: 3.88 GPA, 94.4% A/AB (n=36 letter grades); Spring 2026: 3.74 GPA, 91.7% A/AB (n=48 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Probability, statistics, function fitting, and optimization
  • Supervised/unsupervised learning and neural networks
  • Physics applications: collider data, astronomy, Ising model, string theory

Skills

  • Supervised and unsupervised machine learning techniques, including neural networks
  • Probability theory, statistics, function fitting, parameter inference, and optimization
  • Programming for machine learning, including constructing working programs and debugging
  • Applying ML to specific physics problems such as collider data, astronomy, and statistical mechanics

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2026 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1280 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

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Selected offering source records
PHYSICS 361 · Spring 2027

Machine Learning in Physics

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:754:026564",
    "course_id": "PHYSICS 361",
    "course_uid": "course_84445b017ecd509bab57c075",
    "term_id": "1274",
    "source_course_id": "026564",
    "source_subject_id": "754",
    "title": "Machine Learning in Physics",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]
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Student reviews

Original comments behind the course and instructor summaries.

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No records available.

Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "2267946436d2888287440be826550b59a6b04dcd3b6040b917cdf4498a14a443",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}