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.
Summary
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
↗letter grades
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
- COMPSCI 200
- COMPSCI 220
- place into COMP SCI 300
- take one
- take one
- grad/prof standing
- member of Engr Guest Stdnts
- declared in Capstone Cert in AI for Engr Data Analytics
“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- All of
- Any of
- COMPSCI 200
- COMPSCI 220
- place into COMP SCI 300
- Any of
- Any of
- Any of
- grad/prof standing
- member of Engr Guest Stdnts
- declared in Capstone Cert in AI for Engr Data Analytics
- Any of
Professors
Fall 2026No 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 2026Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
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
Skills
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
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
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
No offering records for the selected term.
Raw records
[]
Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesModel outputs & technical records
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": "cbe646fcfec24c7b6a9ff65844e909c136fd1ea1f3975b5d49b588ae8fe0e334",
"requirements_status": "valid",
"dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
"observed_at": "2026-09-07 15:55:43.033547+00:00"
}