This course was taught and recorded at the University of Miami in 2026 — nine lectures, about nine and a half hours, free to watch, no registration. The recordings follow the same twelve topics as this site, so a deck and a video exist for most of the material.
Watch in order, or jump to whichever topic you need. The right-hand column links the matching deck on the slides page.
| # | Lecture | Length | Covers |
|---|---|---|---|
| 1 | Introduction | 1 h 10 m | Topics 1, 2 — AI for economics research; what quantitative macro needs |
| 2 | Numerical Techniques for Quant Macro | 1 h 11 m | Topic 4 — approximation, optimization, root-finding |
| 3 | Numerical Techniques, continued | 42 m | Topic 4 — interpolation, quadrature, AR(1) discretization |
| 4 | Coding with AI for Quant Macro | 32 m | Topic 3A — the Research Architect workflow with Claude Code |
| 5 | Numerical Dynamic Programming | 1 h 10 m | Topic 7 — VFI and time iteration; closes by bridging to the Aiyagari model (Topic 11) |
| 6 | Advanced Numerical Methods & Accuracy Analysis | 1 h 11 m | Topic 8 — Howard, EGM, Euler equation errors |
| 7 | Perturbation and Projection Methods | 1 h 15 m | Topic 9 — Taylor expansions, Chebyshev, Smolyak |
| 8 | Machine Learning and Deep Learning for Macroeconomists | 1 h 7 m | Topics 5, 6 — PyTorch, then neural solutions to macro models |
| 9 | Solving DSGE Models via Deep Reinforcement Learning | 1 h 5 m | Topics 6, 11 — deep networks on Krusell-Smith, plus policy evaluation/improvement, which is sister-course RL material |
The same material taught at other institutions and in earlier years. Content overlaps with the 2026 series; the earlier series are longer and slower-paced, which some viewers prefer.
The closest match to this syllabus after the 2026 series, and the only place two topics are taught on camera at length:
Lectures 4–6 ("Computation of Dynamic Models
I–III") cover the ground of Topics 7–9.
https://space.bilibili.com/2142649036/lists/5800214
The longest series, and the one that leans hardest on the AI side
of the course.
https://space.bilibili.com/2142649036/lists/5800263
Start here if you want the shape of the whole thing in an
afternoon.
https://space.bilibili.com/2142649036/lists/5800302
Broken into short focused videos, which makes it the easiest to
dip into for one specific method.
https://space.bilibili.com/2142649036/lists/3144093
Separate from the computational series above, a previous offering of Advanced Macroeconomic Theory is recorded in full — the growth model, competitive equilibrium, overlapping generations, asset pricing, fiscal policy, and uncertainty. It is the theory this course puts on a computer, and it is taught in Chinese. See Advanced Macro Theory, or go straight to the series on Bilibili.
The written counterpart to these lectures is 机器学习与数量宏观经济学 (Machine Learning and Quantitative Macroeconomics) by Zhigang Feng, which bridges classical numerical methods and deep learning for dynamic economic models.
These recordings and this site are the foundation stage. The frontier — language models as measurement, retrieval, and agentic research pipelines — is the sister course, AI for Economic Research: Dynamic Models, Language, and Agents.
Classical Methods → ML & Deep Learning → Agentic AI Research
This site is the first two stages.