Recent recorded grades — Fall 2025: 3.71 GPA, 87.3% A/AB (n=63 letter grades). Includes jointly taught sections.
Fall 2026
Machine Learning and Artificial Intelligence Models for Business Analytics
Introduces machine learning and AI models for business analytics, covering supervised and unsupervised techniques.
Recorded instructors · Fall 2026 Zhongtian Chen
Summary
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Recent recorded grades — Fall 2025: 3.71 GPA, 87.3% A/AB (n=63 letter grades); Spring 2026: 3.96 GPA, 97.5% A/AB (n=80 letter grades).
Grade history
↗ average GPA
letter grades
letter grades
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All recorded terms · compare terms & instructors
Prerequisites
Course mapACT SCI 640 or (GEN BUS 656 or concurrent enrollment)
“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
Professors
Fall 2026Historical instructors & teaching patterns
ZHONGTIAN CHEN is recorded teaching in Fall 2025. Recorded history may be incomplete and does not establish a future schedule.
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 |
|---|---|---|---|
| LEC 010 | Classroom Instruction | 28 / 35 | 0 |
| LEC 011 | Classroom Instruction | 20 / 35 | 0 |
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.
Meeting source records
Student experience
the class
Recent recorded grades — Fall 2025: 3.71 GPA, 87.3% A/AB (n=63 letter grades); Spring 2026: 3.96 GPA, 97.5% A/AB (n=80 letter grades).
difficulty & workload
No workload feedback recorded.
Topics
Skills
Grades
Latest available · Spring 2026— not enough history to project Fall 2026.
Grade distribution · % of letter grades
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AB
B
BC
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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
1283 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.
Selected offering source records
Machine Learning and Artificial Intelligence Models for Business Analytics
Recorded 2026-09-07Raw records
[
{
"run_id": "20260907T155543-ce3781c4",
"semester": "1272",
"observed_at": "2026-09-07 15:55:43.033547+00:00",
"offering_id": "1272:231:026426",
"course_id": "GENBUS 657",
"course_uid": "course_d867856b67bb1895c4ab83ea",
"term_id": "1272",
"source_course_id": "026426",
"source_subject_id": "231",
"title": "Machine Learning and Artificial Intelligence Models for Business Analytics",
"credits_min": 2,
"credits_max": 2,
"typically_offered": "Not Applicable"
}
]Rate My Professors
Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
No records available.
Madgrades
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesModel outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
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": "91063df3832c2054a8fd791544ef29cf9952a8746522f2ddd2e140dcd7c21030",
"requirements_status": "valid",
"dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
"observed_at": "2026-09-07 15:55:43.033547+00:00"
}