Machine Learning in Chemistry
Introduction to machine learning in chemistry covering probability, statistics, optimization, and applications like drug discovery and force-fields.
Summary
Recent recorded grades — Spring 2025: 3.97 GPA, 100.0% A/AB (n=15 letter grades); Spring 2026: 3.67 GPA, 93.3% A/AB (n=15 letter grades).
What you’ll be able to do
Employ statistical and machine learning models to analyze chemical datasets.
Represent chemical compounds using molecular descriptors.
Model and predict properties and activities of chemical compounds.
Apply appropriate machine learning approaches to analyze time-series data for molecular systems.
Apply machine-learning-based potential functions to describe chemical and biological molecules.
Be familiar with programming techniques and common tools in machine learning when applied to molecular systems.
Assess the quality of machine learning approaches in the chemistry literature.
Source: UW Guide
Grade history
↗All recorded terms · compare terms & instructors
Prerequisites
Course map(CHEM 103, 109, or 115) and (MATH 234, 320, 331, 340, 345, or 375)
This is a best-effort interpretation; check the catalog requirements above.
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 — Spring 2025: 3.97 GPA, 100.0% A/AB (n=15 letter grades); Spring 2026: 3.67 GPA, 93.3% A/AB (n=15 letter grades).
difficulty & workload
No workload feedback recorded.
Topics
- Probability, statistics, function fitting, and optimization
- Chemical applications: synthesis, force-fields, phase transitions, and drug discovery
Skills
- Statistical and machine learning analysis of chemical datasets
- Representation of chemical compounds via molecular descriptors
- Prediction of chemical compound properties and activities
- Application of ML-based potential functions for molecular systems
Grades
Latest available · Spring 2026— not enough history to project Spring 2027.
Grade distribution · % of letter grades
Grades over time
Through Spring 2026
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
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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": "04a8cc37a56d225e2310e3e4e3932f2f1574b1558abb4ad1acce9ba01bd45c77",
"requirements_status": "valid",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}