Spring 2027

Machine Learning in Action for Industrial Engineers

Machine Learning in Action for Industrial Engineers covers principles, algorithms, and predictive analytics for improving decision-making in healthcare, transportation, and public sectors.

offering recorded3 credits
Recorded instructors · Spring 2027 Andi Wang

Summary

1 / 6

Past student reviews · Ari Smith

Homework and quizzes were considered fair, but project directions were vague and super open-ended, requiring students to talk to the instructor to stay on track.

What you’ll be able to do

  • Identify problems amenable to machine learning and the techniques required to solve those problems (regression vs. classification, regularization, bagging vs. boosting, etc.)

  • Apply appropriate analytical models to solve problems and improve decision-making using Python

  • Effectively communicate findings through both oral and written formats

  • Demonstrate an understanding of how industrial engineering techniques (e.g., optimization) are used to train machine learning models

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

(COMP SCI 200, 220, or placement inCOMP SCI 300),(I SY E 323 or I SY E/​COMP SCI/​E C E 524), and (I SY E 210,STAT 311, 324,STAT/​MATH 309, 409, 431, or MATH 331), grad/prof standing, member of Engr Guest Stdnts, or declared in AI for Engr Data Analytics

ISYE 521 used by

“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

Spring 2027

Recent recorded grades — Spring 2026: 3.99 GPA, 100.0% A/AB (n=49 letter grades).

Historical instructors & teaching patterns

ANDI WANG is recorded teaching in Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

ARI SMITH is recorded teaching in Fall 2022, Fall 2023, Fall 2024, Spring 2025. Recorded history may be incomplete and does not establish a future schedule.

JUSTIN BOUTILIER is recorded teaching in Fall 2021, Fall 2022. 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

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 480
LEC 003Online Only0 / 300

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

Meeting source records
Download part 1

Student experience

the class

Past student reviews · Ari Smith

Students described the course as concept-focused with interesting lectures and a supportive instructor who cared about learning.

Recent recorded grades — Fall 2024: 3.51 GPA, 76.7% A/AB (n=43 letter grades); Spring 2025: 3.80 GPA, 100.0% A/AB (n=10 letter grades); Spring 2026: 3.99 GPA, 100.0% A/AB (n=56 letter grades).

difficulty & workload

Past student reviews · Ari Smith

Homework and quizzes were considered fair, but project directions were vague and super open-ended, requiring students to talk to the instructor to stay on track.

Past student reviews · Ari Smith

The teaching style emphasized overarching concepts rather than details, which students found engaging and helpful for understanding the material.

Topics

  • Statistics, linear and logistic regression, regularization, over-fitting, clustering, trees, boosting, bagging, deep learning, and neural networks.
  • Applications in healthcare, transportation, and the public sector.
  • Predictive analytics focusing on combining data and models for better decision-making.

Skills

  • Identify ML-suitable problems and select appropriate techniques like regression, classification, regularization, bagging, or boosting.
  • Apply analytical models in Python to solve problems and enhance decision-making processes.
  • Understand how industrial engineering techniques such as optimization are used to train machine learning models.
  • Communicate findings effectively through oral and written formats.

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.

Download part 1
Selected offering source records
ISYE 521 · Spring 2027

Machine Learning in Action for Industrial Engineers

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": "1274:490:025993",
    "course_id": "ISYE 521",
    "course_uid": "course_1aa7cc66114a83c08d6cf947",
    "term_id": "1274",
    "source_course_id": "025993",
    "source_subject_id": "490",
    "title": "Machine Learning in Action for Industrial Engineers",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
Download part 1
Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
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": "3a232de6ead95d61c0dae7b90f9c4d0d668748ce1cf36cdbb64ad3fc4b85702f",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}