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: 01_Intro_to_DL | L02 01--09 (c9) |
| 2 | Deep Equilibrium Nets | L03: 02_DEQNs; L07: 05b_AutoDiff | L03 01--02 (c), 03--04 (e/s), 05 (c); L07 01--04 (c) |
| 3 | IRBC Model | L04: 03_IRBC | L04 01--02 (c) |
| 4 | NAS & Loss Norm. | L05: 04_NAS, 05_Loss | L05 02--04 (c), 05 (e) |
| 5 | OLG Models | L08: 08_OLG | L08 07--10 (c), 11 (e) |
| 6 | HA, Young, Seq. Space | L09: 09_HA_Young; L10: 10_SeqSpace | L09 10--12 (c); L10 05, 05b, 06 (c), KrusellSmith_Tutorial_CPU (x) |
| 7 | PINNs | L11: 06_PINNs | L11 01--05 (c) |
| 8 | CT Het. Agents | L12: 07_CT_Theory; L13: 08_CT_Num | L13 06--08 (c), 09 (e) |
| 9 | Surrogates, GPs, DKL | L14: 07_Surrogates_GPs | L14 01, 02, 04--08 (c), 07, 09, 10 (x) |
| 10 | Structural Estimation | L15: 08_Struct_Est | L15 03, 03b (c) |
| 11 | Climate & Deep UQ | L16: 08_Climate; L17: 09_UQ | L16 01--03 (c); L17 09_DICE_2P_UQ_Analysis (c) plus 4 .py pipeline drivers |
| 12 | Synthesis & Outlook | L18: 10_Wrap_Up | --- |
Path conventions. The repository organizes lectures by stable block id (lectures/lecture__B_*/); the leading L in the table is the student-facing lecture number and the parenthetical B is the canonical block id (which is stable across renumberings). Slide and notebook names in the table are abbreviated; full paths follow lectures/lecture__B_*/{slides,code}/. 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.
Workshop material. The live course includes a hands-on workshop on agentic programming (using AI agents as coding partners), delivered as L06. Because this field is evolving quickly, it is presented through slides, two Python helper scripts, and exercises rather than as a fixed manuscript chapter. The manuscript remains organized chapter by chapter, with the workshop material collected in the L06 slides and exercise prompts.
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.