Dates tentative — confirm before publishing. All meetings are online. Recordings and materials post to the course Teams site within 24 hours.
The eight meetings
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Meeting 1 · Week 1 · Wed 28 Oct
Welcome & What Is Urban AI?
Orientation (30 min) — introduction to the learning experience, privacy and data rules, the AI Use Log, and the AI readiness pre-survey.
Class 1-1 (60 min) — urban analytics and smart cities to urban AI; how planning decisions get made; what a decision support system is. Si Chen
Discussion Forum A launches (30 min) — where is Urban AI already deciding things? -
Meeting 2 · Week 1 · Mon 2 Nov
AI Basics for Urban Problems
Class 1-2 (60 min) — data, features, models, training and inference; machine learning. Jie Hou
Lab (60 min) — clustering analysis. Jie Hou · Sean Tian -
Meeting 3 · Week 2 · Wed 4 Nov
Urban Analytic Models I
Class 2-1 (60 min) — urban data sources, site analysis. Si Chen
Lab (60 min) — spatial analysis and clustering. Sean Tian -
Meeting 4 · Week 2 · Mon 9 Nov
Urban Analytic Models II
Class 2-2 (60 min) — AI-augmented urban analytic models. Si Chen
Lab (60 min) — time series analysis. Sean Tian -
Meeting 5 · Week 3 · Wed 11 Nov
Decision Support Systems I
Class 3-1 (60 min) — decision support systems (DSS) — data, model, design, and application. Jie Hou & Si Chen
Lab (60 min) — risk-response DSS. Yura Gao -
Meeting 6 · Week 3 · Mon 16 Nov
Decision Support Systems II
Class 3-2 (60 min) — AI-enhanced decision support systems. Jie Hou
Lab (60 min) — DSS AI Lab. Yura Gao -
Meeting 7 · Week 4 · Wed 18 Nov
Language Models in Decision Support
Class 4-1 (60 min) — what language models are useful for in the decision-making process. Yijia Wen
Lab (60 min) — text mining and clustering on planning documents. Yijia Wen · Sean Tian -
Meeting 8 · Week 4 · Mon 23 Nov
AI Ethics and Wrap-Up
Class 4-2 (45 min) — AI ethics: bias, equity, privacy, transparency, accountability. Jie Hou
Discussion Forum B & reflection (30 min) — how human–AI interaction will change decision-making in planning; conclusion of the learning experience, and the post-survey.
What a meeting looks like
| Time | Segment | Who |
|---|---|---|
| 0:00 – 0:05 | Arrival and recap of where we are | Instructor |
| 0:05 – 0:55 | Concept class, with two check-for-understanding pauses | Instructor |
| 0:55 – 1:05 | Break | — |
| 1:05 – 1:25 | TA demo and explanation of the lab | TA |
| 1:25 – 1:50 | Breakout: teams run the lab and change parameters | Teams + TA |
| 1:50 – 2:00 | Report-outs and reflection prompts | All |
Every lab runs in the browser. There is nothing to install and no coding required.
Attendance and participation
Attend at least 7 of the 8 meetings. Short write-ups and lab outputs are encouraged but not required — the teaching team gives feedback on anything you submit, and submitting is the fastest way to get it. Completing students receive a non-credit certificate and a Spartan Experience Record entry.