Artificial Intelligence (ai) in Systems
Explores deploying AI in systems at scale responsibly, applying and analyzing AI in industrial environments like transportation, manufacturing, and healthcare.
Summary
Lectures are small, with a median of 28 enrolled students in the recorded sections.
What you’ll be able to do
Explain the basic principles and techniques of artificial intelligence.
Evaluate AI systems using various metrics and methodologies.
Represent engineered systems using mathematical models.
Assess the reliability and robustness of AI systems in complex industrial environments.
Analyze privacy concerns and ethical considerations in AI applications in engineering.
Discuss the importance of transparency and interpretability in AI systems.
Apply AI techniques to solve real-world problems involving engineered systems in complex industrial environments.
Source: UW Guide
Grade history
↗No recorded grade history.
All recorded terms · compare terms & instructors
Prerequisites
Course map(MATH/STAT 309,STAT 311,MATH/STAT 431 or MATH 531) and (I SY E 210,E C E 331,STAT/MATH 310,STAT 312, 324, or 371), or graduate/professional standing
- take one
- take one
- graduate/professional standing
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
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- All of
- graduate/professional standing
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Spring 2027Schedule 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
Student experience
the class
Exploration of deploying artificial intelligence (AI) in systems at scale and doing so responsibly. Application and analysis of AI in diverse industrial environments in fields such as transportation, manufacturing, and healthcare.
difficulty & workload
No workload feedback recorded.
Topics
- Industrial AI applications in transportation, manufacturing, and healthcare.
- Privacy, ethics, transparency, and interpretability in AI.
- Mathematical modeling of engineered systems.
Skills
- Explain basic principles and techniques of artificial intelligence.
- Evaluate AI systems using various metrics and methodologies.
- Represent engineered systems using mathematical models.
- Assess the reliability and robustness of AI systems in complex industrial environments.
- Analyze privacy concerns and ethical considerations in AI applications in engineering.
- Apply AI techniques to solve real-world problems involving engineered systems in complex industrial environments.
Grades
Grade distribution · % of letter grades
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More grade details Grade mix, volume & source data
Sources & history
Catalog & offerings
Descriptions, prerequisites, and recorded course offerings.
Catalog observation history
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Raw records
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Student reviews
Original comments behind the course and instructor summaries.
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Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "5fe48470c9d4bdf99736447d5b8dadc4ada348eca5da1f03932dc3300228999a",
"requirements_status": "valid",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}