AI for Economic Research: Dynamic Models, Language, and Agents was taught by Zhigang Feng as a 24-hour graduate intensive over six meetings in July 2026. Announcements and Q&A ran through a course WeChat group (群聊：湖畔宏观); that group invite expired on 12 July 2026 and is not reproduced here.
| Date | Time | Location | Session |
|---|---|---|---|
| Mon, July 6 | Morning 8:50–12:25 | 北1-306 | Session 1 — Foundations (Lec 1–2) |
| Tue, July 7 | Afternoon 14:00–17:00 | 经济学院121报告厅 | Session 2 — ML Essentials (Lec 3) |
| Wed, July 8 | Morning 8:50–12:25 | 北1-306 | Session 3 — Deep Learning for Macro (Lec 4) |
| Thu, July 9 | Morning 9:00–12:00 | 经济学院121报告厅 | Session 4 — RL & Heterogeneous Agents (Lec 5–6) |
| Fri, July 10 | Morning 8:50–12:25 | 北1-306 | Session 5 — LLMs & Text (Lec 7–8) |
| Sun, July 12 | Morning 8:50–12:25 | 北1-306 | Session 6 — Agentic AI & Case Studies (Lec 9–10) |
The six meetings map one-to-one onto the six modules the site is now organized around, so anything written for the 2026 cohort still points at the right material:
| 2026 session | Now | Lectures |
|---|---|---|
| Session 1 | Module 1 — Foundations | 1–2 |
| Session 2 | Module 2 — Machine Learning Essentials | 3 |
| Session 3 | Module 3 — Deep Learning for Macro Models | 4 |
| Session 4 | Module 4 — RL & Heterogeneous Agents | 5–6 |
| Session 5 | Module 5 — Language Models & Text | 7–8 |
| Session 6 | Module 6 — Agentic AI & Case Studies | 9–10 |
Students worked in teams of two to four on one of four tracks and submitted a reproducibility
package — paper, code repository, AI_LOG.md, a 12–15 minute talk, a
provenance package, and a referee report on another team's project — assessed on a 100-point
rubric with a bonus for a validated stretch finding. Tracks B, C and D drew on unpublished
research and carried confidentiality obligations for that cohort.
The current project tracks keep the same four projects, the same replicate-first rule, and the same validation checklists, but are written for self-directed work: the rubric became a self-assessment, the milestones became suggested pacing, and every track now runs on public data only.