Fall 2026

Machine Learning in Action for Industrial Engineers

ISYE 521 teaches machine learning principles and algorithms for industrial engineering, focusing on predictive analytics and combining data with models to improve decision-making.

no offering record for this termCredits unavailable
Recorded instructors · Fall 2026 No instructors listed

Summary

1 / 5

Historical reviews of Ari Smith: Homework and quizzes were fair, but the open-ended project required students to verify their approach with the instructor to stay on track.

Grade history

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

Prerequisites

Course map

(COMP SCI 200, 220, or place into COMP 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, or 431), grad/prof standing, member of Engr Guest Stdnts, or declared in Capstone Cert 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

Fall 2026

No instructors recorded for this selection.

Historical instructors & teaching patterns

Historical reviews of Justin Boutilier: Justin Boutilier is described as an exceptional instructor, with one reviewer calling him the best they have ever had.

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

Fall 2026

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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

No records available.

Student experience

the class

Historical reviews for Justin Boutilier and Ari Smith describe engaging instruction and high quality, though one reviewer notes vague project directions under Smith.

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

Historical reviews of Ari Smith: Homework and quizzes were fair, but the open-ended project required students to verify their approach with the instructor to stay on track.

Historical reviews of Ari Smith: Lectures focused on overarching concepts and were interesting, but the lack of specific project guidance could be frustrating without proactive communication.

Topics

  • Predictive analytics and decision-making.
  • Statistical methods, regression, regularization, clustering, trees, boosting, bagging, deep learning, and neural networks.
  • Applications in healthcare, transportation, and the public sector.

Skills

  • Combining data and models to improve decision-making.
  • Applying statistical and machine learning methods including regression, clustering, trees, boosting, bagging, deep learning, and neural networks.

Grades

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

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

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AB
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BC
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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

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.

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Selected offering source records

No offering records for the selected term.

Raw records 
[]
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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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Dataset & model

Recorded 2026-09-07

View the dataset on Hugging Face
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": "cbe646fcfec24c7b6a9ff65844e909c136fd1ea1f3975b5d49b588ae8fe0e334",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}