The following table maps each manuscript chapter to its companion slide deck(s) and Jupyter notebooks. All paths are relative to the repository root; the short names are intentionally compact so the table remains readable.
Execution map: manuscript chapters, slides, and notebooks
| Ch. | Topic | Lecture folder & deck | Notebooks (role) |
|---|---|---|---|
| 1 | Intro to ML & DL | L02: intro_deep_learning | L02 01–09 (c9) |
| 2 | Deep Equilibrium Nets | L03: deep_equilibrium_nets; L07: autodiff_for_deqns | L03 01–02 (c), 03–04 (e/s), 05 (c); L07 01–04 (c) |
| 3 | IRBC Model | L04: irbc | L04 01–02 (c) |
| 4 | NAS & Loss Norm. | L05: neural_architecture_search, loss_normalization | L05 02–04 (c), 05 (e) |
| 5 | OLG Models | L08: olg_models_deqns | L08 07–10 (c), 11 (e) |
| 6 | HA, Young, Seq. Space | L09: heterogeneous_agents_youngs; L10: sequence_space_deqns | L09 10–12 (c); L10 05, 05b, 06 (c), KrusellSmith_Tutorial_CPU (x) |
| 7 | PINNs | L11: pinns | L11 01–05 (c) |
| 8 | CT Het. Agents | L12: continuous_time_ha_theory; L13: continuous_time_ha_numerics | L13 08 (c) |
| 9 | Surrogates, GPs, DKL | L14: surrogates_and_gps | L14 01, 02, 04–08 (c), 09, 10 (x) |
| 10 | Structural Estimation | L15: structural_estimation_smm | L15 03, 03b (c) |
| 11 | Climate & Deep UQ | L16: climate_economics_iams; L17: deep_uq_pareto_policy | L16 01–03 (c); L17 09_DICE_2P_UQ_Analysis (c) plus 4 .py pipeline drivers |
| 12 | Synthesis & Outlook | L18: wrap_up | — |
Path conventions. Lecture folders are lectures/lecture__*/, where L in the table is the lecture number; the slide sources live in that folder’s slides/ subfolder and the notebooks in code/. Names in the table are abbreviated: deck names drop the lecture__ prefix that every slide file carries, and a notebook is cited by the number that follows that prefix, so L03 01 is lectures/lecture_03_deep_equilibrium_nets/code/lecture_03_01_Brock_Mirman_1972_DEQN.ipynb. Notebook role letters: c = core, e = exercise, s = solution (paired with an exercise notebook), x = extension/self-study. See the README for complete file names and direct links.
Lectures without a chapter. Two lectures have no manuscript chapter of their own. L01 (lecture_01_python_primer) is a self-contained Python primer for readers who want a refresher before Chapter 1. L06 (lecture_06_agentic_programming) is a hands-on workshop on agentic programming (using AI agents as coding partners); because this field is evolving quickly, it is presented through slides, two Python helper scripts, and exercise prompts rather than as a fixed manuscript chapter.
Reproducibility. Random-seed conventions, the RUN_MODE budget split, hardware and software pins, and GPU-determinism flags used by every notebook in the table above are documented in Appendix Appendix E. Worked solutions and guidance for the end-of-chapter exercises are collected in Appendix Appendix F.