Fall 2026

Machine Learning in Chemistry

Introduces machine learning techniques in chemistry, covering probability, optimization, and applications like generative models and drug discovery.

offering recorded3 credits

Recorded instructors · Fall 2026 Xuhui Huang

Summary

1 / 3

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).

Grade history

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average GPA
letter grades

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Prerequisites

Course map

(CHEM 103, 109, or 115) and (MATH 234, 320, 331, 340, or 375)

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CHEM 361

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Professors

Fall 2026

Xuhui Huang

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Historical instructors & teaching patterns

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Calendar & sections

Fall 2026

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Section	Mode	Enrolled / capacity	Waitlist
LEC 001	Classroom Instruction	12 / 25	0

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 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, parameter inference, optimization
Generative models, force-fields, phase transitions, molecular dynamics, drug discovery

Skills

Probability, statistics, function fitting, parameter inference, and optimization
Applying machine learning to chemical systems and drug discovery

Grades

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

Grade distribution · % of letter grades

Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Not enough comparable courses for Fall 2026 in UW–Madison.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

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Selected offering source records

CHEM 361 · Fall 2026

Machine Learning in Chemistry

Recorded 2026-09-07

Raw records

[
  {
    "run_id": "20260907T155543-ce3781c4",
    "semester": "1272",
    "observed_at": "2026-09-07 15:55:43.033547+00:00",
    "offering_id": "1272:224:026798",
    "course_id": "CHEM 361",
    "course_uid": "course_63f398e50e0bc4980d5d168f",
    "term_id": "1272",
    "source_course_id": "026798",
    "source_subject_id": "224",
    "title": "Machine Learning in Chemistry",
    "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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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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Model outputs & technical records

nvidia/Qwen3.6-35B-A3B-NVFP4

LLM outputs across runs

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Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance

{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "dc2042d2fa0d5e4f29a9f1ea46fec31abf23e9d2be176fdbbdf757cd5744dec8",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}