Machine Learning in Materials
MS&E 561 introduces data science applications in materials science, focusing on data resources and machine learning integration.
Summary
Recent recorded grades — Fall 2025: 4.00 GPA, 100.0% A/AB (n=15 letter grades).
Grade history
↗letter grades
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
Fall 2026No instructors recorded for this selection.
Historical instructors & teaching patterns
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Fall 2026Schedule 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
No records available.
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
Skills
Grades
Latest available · Fall 2025— not enough history to project Fall 2026.
Grade distribution · % of letter grades
More grade details Grade mix, volume & source data
Not enough comparable courses for Fall 2026 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
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Raw records
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Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
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Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesModel outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
"model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
"task_version": "14",
"output_id": "17e11a06f9c08c855ed533832973d91c2b0e8b4c79c9b1d8be06ff16ee6d7fa8",
"requirements_status": "valid",
"dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
"observed_at": "2026-09-07 15:55:43.033547+00:00"
}