Spring 2027

Machine Learning and Artificial Intelligence Models for Business Analytics

Introduction to machine learning and AI models for business analytics, covering supervised learning, additive models, CARTs, bagging/boosting, deep learning, and unsupervised techniques like clustering.

offering recorded2 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 4

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).

What you’ll be able to do

  • Build machine learning models for the purpose of prediction in business contexts, choosing from a variety of approaches.

  • Tune models to optimize predictive performance in business settings.

  • Evaluate and compare prediction accuracy of regression and classification models in business settings.

  • Decompose and demystify machine learning models that underpin AI-driven business solutions.

  • Identify more advanced literature on machine learning for business applications and be able to develop skills in self-study.

Source: UW Guide

Grade history

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average GPA
letter grades
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All recorded terms · compare terms & instructors

Prerequisites

Course map

ACT SCI 640 or (GEN BUS 656 or concurrent enrollment)

GENBUS 657 used by

“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.

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Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

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SectionModeEnrolled / capacityWaitlist

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.

Meeting source records
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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

  • Supervised machine learning algorithms including additive models, CARTs, bagging/boosting, and deep learning.
  • Unsupervised learning techniques such as clustering and anomaly detection.
  • Machine learning models underpinning AI-driven business solutions.

Skills

  • Developing general algorithmic prediction models for supervised machine learning.
  • Applying additive models, CARTs, bagging/boosting, deep learning, and AI models.
  • Understanding unsupervised learning techniques including clustering and anomaly detection.
  • Evaluating and comparing prediction accuracy of regression and classification models.

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

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

1280 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.

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Selected offering source records
GENBUS 657 · Spring 2027

Machine Learning and Artificial Intelligence Models for Business Analytics

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:231:026426",
    "course_id": "GENBUS 657",
    "course_uid": "course_d867856b67bb1895c4ab83ea",
    "term_id": "1274",
    "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

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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "4080bad19a4b76bc06650a5749feb43394b239c31a6e2b1b0fa3a2371bc88122",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}