Machine Learning in Materials
Introductory course on applying data science and machine learning to materials science, covering data resource creation, data-centric approaches, and ML workflow development.
Summary
Recent recorded grades — Fall 2025: 4.00 GPA, 100.0% A/AB (n=15 letter grades).
What you’ll be able to do
Recall and utilize basic principles and methods of how informatics (data science and machine learning) can be used in materials science and engineering
Apply software tools related to materials informatics
Communicate effectively about technical work in oral and written presentation
Develop machine learning workflows for informatics applications in materials science and engineering
Source: UW Guide
Grade history
↗All recorded terms · compare terms & instructors
Prerequisites
Course mapSatisfied Quantitative Reasoning (QR) B, graduate/professional standing, or member of Engineering Guest Students
- Satisfied Quantitative Reasoning (QR) B
- graduate/professional standing
- member of Engineering Guest Students
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Any of
- Satisfied Quantitative Reasoning (QR) B
- graduate/professional standing
- member of Engineering Guest Students
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
Student experience
the class
Recent recorded grades — Fall 2025: 4.00 GPA, 100.0% A/AB (n=15 letter grades).
difficulty & workload
No workload feedback recorded.
Topics
- Creation and use of modern data resources
- Data-centric approaches to materials
- Integration of machine learning in materials science
Skills
- Applying data science and machine learning principles to materials science
- Using software tools for materials informatics
- Developing machine learning workflows
Grades
Latest available · Fall 2025— not enough history to project Spring 2027.
Grade distribution · % of letter grades
More grade details Grade mix, volume & source data
Not enough comparable courses for Spring 2027 in UW–Madison.
Sources & history
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.
Selected offering source records
No offering records for the selected term.
Raw records
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Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
No records available.
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "0e409766decaee811d68eb6bfe75c8183ae14abaffccaab2cc28b3f418126992",
"requirements_status": "needs_review",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}