🧠 Machine Learning • 🔢 Mathematics • ⚛️ Physics • 🧬 Biology • 📈 Economics • 💭 Philosophy • 🔌 PCB Design • ₿ Bitcoin • 🏗️ Software Engineering
Nine self-study curricula, 129 sections in all. They run from foundations to current research in machine learning, mathematics, physics, biology, economics, philosophy of mind, PCB design, Bitcoin and software engineering.
Each section works like a small course:
- why the topic matters, and what to know first
- learning objectives
- a core path of at most three must-read resources
- practice exercises, most with answers you can check
- a mini-project
- self-test questions
Citations and links were audited in the October 2026 review, which also lists the few pages that couldn't be checked automatically.
- Get your own copy and a private checklist. Clone the repo, then run
python tools/progress.py initonce (Python 3.11+). It createsmy/, a folder git ignores, with your own checklist for every curriculum. Your progress stays yours, and pulling updates never conflicts with it. More below. - Pick a topic from the table below and open its curriculum page. Its learning path lists every section in the order to take it, with each section's core path.
- Open the first section. Read why it matters, its prerequisites and the 🎯 learning objectives.
- Follow the core path. The first line of each section's Suggested Order names its 🔑 must-reads, at most three. Read them first, in that order. The numbered list under it is the full-depth order and includes optional items. For each exercise you pick, also read the sources named in its closing (~time · sources) note.
- Do at least two 🛠️ exercises. Each says what to produce, and most say how to check it. Do the 🏗️ mini-project, the section's capstone, when the section is on your track.
- Answer ✅ Check Yourself from memory, in writing, before you look anything up. Then correct your answers against the sources.
- Record it. Tick what you did in
my/progress/<curriculum>.md, and write a one-page note inmy/notes/(template).python tools/progress.py statusshows your progress bars, andpython tools/progress.py nextsays what to do next.
The Study Guide explains why this loop works and adds spaced review. It also has prompts for having NotebookLM or Claude quiz you on a section.
| Curriculum | What you'll learn | Sections | Start here | Anchor resources |
|---|---|---|---|---|
| 🧠 Machine Learning | From perceptrons to LLMs, RL and interpretability. Build and train models from scratch, read current papers critically, and put models on microcontrollers. | 15 | 00 Historical Foundations | Karpathy's Zero to Hero, Prince's Understanding Deep Learning, Sutton & Barto, Ilya Sutskever's reading list |
| 🔢 Mathematics | Proofs, linear algebra, analysis, probability, optimization and information theory, plus an engineering spine: signals and systems, numerical methods, and number theory for cryptography. | 15 | 00 Mathematical Thinking | Axler, Strang, Abbott, Boyd & Vandenberghe, Cover & Thomas, Strogatz |
| ⚛️ Physics | Mechanics through quantum computing, with computation throughout, plus measurement, noise and sensor physics for instrument work. | 13 | 00 Classical Mechanics | The Feynman Lectures, Griffiths, Schroeder, Newman, Nielsen & Chuang, Taylor's Error Analysis |
| 🧬 Biology | Molecular biology through protein design and AI for biology, with the statistics, instrumentation and population genetics you need to judge the numbers. | 15 | 00 Molecular Biology Fundamentals | Alberts, Lehninger, Holmes & Huber, AlphaFold, ESM-2, RFdiffusion |
| 📈 Economics | Incentives, games, decisions and mechanism design, plus causal inference, macro and money, industrial organization, and the economics of AI. | 16 | 00 Economic Thinking | Varian, Tadelis, Angrist & Pischke, Kahneman (with replication caveats), Roughgarden |
| 💭 Philosophy | Mind, consciousness, computation, AI ethics and alignment, with each debate argued from both sides, plus philosophy of science. | 13 | 00 Foundations of Philosophy | Hofstadter, Chalmers, Dennett, Kim, Russell, Godfrey-Smith |
| 🔌 PCB Design | From electronics fundamentals in KiCad to signal integrity, EMC, precision analog layout, and board bring-up and test. | 14 | 00 Electronics Fundamentals | The Art of Electronics, Phil's Lab, Robert Feranec, Rick Hartley's reading list, Bogatin, Ott |
| ₿ Bitcoin | The protocol from the cryptography up: transactions, wallets, P2P, Lightning and governance, with hands-on labs run alongside. | 14 | 00 History and Philosophy | The whitepaper, Mastering Bitcoin, Programming Bitcoin, Learn Me A Bitcoin, Chaincode Labs |
| 🏗️ Software Engineering | Python and embedded C at a senior level: reading and reviewing code, architecture, testing, firmware debugging, hardware-in-the-loop (HIL) automation, and agentic AI workflows. | 14 | 00 Computer Science Foundations | Fluent Python, Modern C, Making Embedded Systems, Designing Data-Intensive Applications, the METR study of AI coding productivity |
The curricula connect. These five tracks thread them together. The Study Guide has each one with links, plus a map of which topic lives where.
| Track | For | Route |
|---|---|---|
| A. Precision hardware and instruments | Measurement hardware: the math, physics, layout and firmware behind it | math/02 → 07 → 03 → 12 → 14 · pcb/00–05 → 12 → 06–09 → 13 · physics/03 → 12 · software/01 → 02 → 07 → 12 → 05 → 13 |
| B. AI and machine learning | Understanding the models you build with | math/01, 03, 04, 05 → ml/00–10 → 12 → 11, with ml/14 as the bridge to hardware |
| C. Bitcoin | The protocol, built on real cryptography | math/08 → 13 → btc/00–10 with the btc/12 labs alongside → btc/13 → btc/11 |
| D. AI for science | Curiosity | physics/06 → 11 · ml/13 · bio/00–04 (with bio/12 and 14) → 07 → 10 → 11 |
| E. Thinking tools | Background reading | philosophy/00 → 12 → econ/00 and 03 → philosophy/01–02 → 05 |
Every numbered section follows the same plan:
| Part | What it gives you |
|---|---|
| Why it matters · Prerequisites | The opening: whether to take the section now, and what to study first |
| 🎯 Learning Objectives | 4–5 things you'll be able to do: your test for "done" |
| Resources | Books, papers, courses and videos, with authors, years and a line on why each is there |
| Suggested Order | Opens with the Core path line, then puts everything else in order |
| 🛠️ Practice + 🏗️ Mini-project | 4–6 exercises with honest time estimates and, mostly, checkable results; then a capstone project |
| ✅ Check Yourself | 8 questions to answer from memory, then check against the sources |
| Marker | Meaning |
|---|---|
| 🔑 | Core path: at most 3 per section, named in the section's Core path line |
| ⭐ | A named, closed list: ml Ilya Sutskever's 27 items · pcb Rick Hartley's 12 books · btc the Bitcoin canon table |
| 🤖 | (software) A resource specific to AI-assisted development |
| A caveat to read before relying on it: a failed replication, a disputed claim, or a link problem |
Each list follows its curriculum's recommended order. Click a curriculum to expand it.
🧠 Machine Learning · 15 sections · curriculum page
- 00 — Historical Foundations of AI · Start here
- 01 — Mathematical Foundations
- 02 — Classical Machine Learning
- 03 — Neural Network Fundamentals
- 04 — CNNs and Computer Vision
- 05 — RNNs, Sequences, and Memory
- 06 — Transformers and Attention
- 07 — Generative Models
- 08 — Scaling, Efficiency, and Deployment
- 14 — Edge and Embedded ML (TinyML) · After 08 (also needs 02 and 04)
- 09 — Large Language Models
- 10 — Reinforcement Learning
- 12 — Interpretability and Alignment · After 10 (needs 06 and 09)
- 11 — Frontiers (2023-2026+) · After 12 · the living section: revisit it
- 13 — Graphs, Geometry, and Science · Any time after 06
🔢 Mathematics · 15 sections · curriculum page
- 00 — Mathematical Thinking · Start here
- 01 — Linear Algebra
- 02 — Calculus and Analysis
- 03 — Probability and Statistics
- 04 — Optimization
- 05 — Information Theory
- 06 — Graph Theory and Combinatorics · Elective
- 07 — Differential Equations
- 12 — Fourier Analysis, Signals and Systems · After 03 and 07 · core for hardware
- 14 — Numerical Methods and Scientific Computing · After 07 or 12
- 08 — Abstract Algebra
- 13 — Number Theory and Cryptography · After 08 · core for Bitcoin
- 09 — Topology and Geometry · Elective
- 10 — Causal Inference · Elective (needs 03 and 06)
- 11 — Category Theory and Frontiers · Elective
⚛️ Physics · 13 sections · curriculum page
- 00 — Classical Mechanics · Start here
- 01 — Waves and Optics
- 02 — Thermodynamics and Statistical Mechanics
- 03 — Electromagnetism
- 04 — Quantum Mechanics
- 05 — Special and General Relativity
- 06 — Computational Physics
- 07 — Quantum Computing
- 08 — Condensed Matter and Materials
- 12 — Measurement, Noise, and Sensor Physics · Right after 08
- 09 — Fluid Dynamics and Continuum Mechanics
- 10 — Nonlinear Dynamics and Chaos
- 11 — Physics-Informed ML and Frontiers
🧬 Biology & Computational Life Sciences · 15 sections · curriculum page
- 00 — Molecular Biology Fundamentals · Start here
- 01 — Biochemistry
- 02 — Genetics and Genomics
- 14 — Evolution and Population Genetics · After 02
- 03 — Structural Biology
- 13 — Bioinstrumentation, Imaging and Lab Automation · After 03
- 12 — Statistics and Experimental Design for Biology · Before 04, or alongside it
- 04 — Bioinformatics and Sequence Analysis
- 05 — Systems Biology
- 06 — Computational Chemistry and Drug Discovery
- 07 — Protein Engineering and Design
- 08 — Genomic Medicine
- 09 — Neuroscience
- 10 — AI for Biology
- 11 — Frontiers (2023–2026+)
📈 Economics, Decision Theory & Game Theory · 16 sections · curriculum page
- 00 — Economic Thinking · Start here
- 01 — Microeconomics
- 13 — Empirical Economics and Causal Inference · After 01
- 02 — Game Theory
- 03 — Decision Theory
- 04 — Mechanism Design
- 05 — Information Economics
- 06 — Behavioral Economics
- 07 — Network Economics
- 15 — Industrial Organization and Strategy · After 07 (needs 01 and 02)
- 08 — Computational Economics
- 09 — Social Choice and Voting
- 14 — Macroeconomics, Money and Financial Markets · Before 10
- 10 — Monetary Economics and Crypto
- 11 — AI Alignment through an Economic Lens
- 12 — Economics of AI · After 11 (needs 13)
💭 Philosophy of Mind & Intelligence · 13 sections · curriculum page
- 00 — Foundations of Philosophy · Start here
- 12 — Philosophy of Science and Scientific Reasoning · Right after 00
- 01 — Philosophy of Mind
- 02 — Consciousness
- 03 — Computation and Mind
- 04 — Intelligence and Understanding
- 05 — Philosophy of AI
- 06 — Ethics of AI
- 07 — Existential Risk and Alignment
- 08 — Language and Meaning
- 09 — Free Will and Agency
- 10 — Eastern Philosophy and Mind
- 11 — Frontiers
🔌 PCB Design · 14 sections · curriculum page
- 00 — Electronics Fundamentals · Start here
- 01 — PCB Anatomy and Materials
- 02 — Schematic Capture and Library Management
- 03 — Component Selection and Sourcing
- 04 — PCB Layout Fundamentals
- 05 — Power Distribution and Grounding
- 12 — Analog, Mixed-Signal and Precision Measurement · After 05
- 06 — Signal Integrity
- 07 — EMC and Compliance
- 08 — Multi-Layer Design and Stackup
- 09 — Design for Manufacturing (DFM) and Fabrication
- 13 — Bring-up, Test and Debug · After 09
- 10 — Advanced Topics
- 11 — Reference Projects and Open-Source Hardware
₿ Bitcoin · 14 sections · curriculum page
- 00 — History and Philosophy · Start here
- 01 — Cryptography Foundations
- 02 — Bitcoin Protocol Fundamentals
- 12 — Hands-on Labs · Start with 02 and run alongside 02–11; each lab names the section it pairs with
- 03 — Mining and Consensus
- 04 — Transactions and Script
- 05 — Wallets and Key Management
- 06 — Network and P2P Layer
- 07 — Lightning Network
- 08 — Security and Privacy
- 09 — Economics and Game Theory
- 10 — Development and Programming
- 13 — Governance and Protocol Change · After 10, before 11
- 11 — Advanced Topics and Frontiers
🏗️ Software Engineering · 14 sections · curriculum page
- 00 — Computer Science Foundations · Start here
- 01 — Python Mastery
- 02 — Embedded C Mastery
- 03 — Reading and Reviewing Code
- 04 — Software Architecture and Design Patterns
- 05 — Testing and Quality Assurance
- 06 — Systems Design and Infrastructure
- 07 — Embedded Systems and RTOS
- 12 — Firmware Toolchain, Debug, and Observability · After 07
- 13 — Hardware-in-the-Loop Testing and Lab Automation · After 12 (or after 05)
- 08 — Security and Reliability Engineering
- 09 — Agentic AI Development Workflows · Start early and run alongside (needs 01 and 02)
- 10 — Version Control and Collaboration · Start early and run alongside
- 11 — Professional Practice and Technical Leadership
Your progress lives in my/, a folder git ignores. Nothing you tick, write or build there is ever committed to this repo, so you can pull updates whenever you like.
git clone https://github.com/aqsnyder/learning.git && cd learning
python tools/progress.py init # once: my/progress/, my/notes/, my/projects/, my/log.csv
python tools/progress.py next pcb # the next unfinished section, and what's left in it
python tools/progress.py status # progress bars for every curriculum (--sections for detail)
python tools/progress.py log pcb/05 3.5 # record study time; status shows your hours
git pull && python tools/progress.py sync # after updating: new items appear, your ticks are kept- Checklists:
my/progress/<curriculum>.mdhas a box for every resource, every exercise, the mini-project, three Check Yourself passes (now, a week later, a month later) and your section note. Tick by changing- [ ]to- [x]in any editor. A section counts as done when its 🔑 core items, two exercises and the first Check Yourself pass are ticked. - Notes and projects: one note per section in
my/notes/(template), and exercise and mini-project work inmy/projects/(template). - Back it up:
python tools/progress.py init --gitmakesmy/its own git repository. Push it to a private remote.
The PROGRESS.md file in each curriculum is the blank template that init and sync copy from. Don't tick boxes there.
learning/
├── README.md ← you are here
├── STUDY-GUIDE.md ← the study loop, five tracks, how the curricula connect, one home per topic
├── ml/ ← each curriculum has the same layout:
│ ├── README.md ← overview, legend, learning path in recommended order
│ ├── PROGRESS.md ← blank checklist template (generated; your copy lives in my/)
│ ├── extras.toml ← books and courses with no section home
│ ├── 00-historical-foundations/
│ │ ├── README.md ← the section: objectives, resources, suggested order, practice, questions
│ │ └── resources.toml ← the section's resources as data; the README's table is generated from it
│ ├── ... ← numbered sections (ml: 00–14)
│ └── books/ courses/ blogs-and-references/
├── math/ ← 00–14
├── physics/ ← 00–12
├── bio/ ← 00–14
├── econ/ ← 00–15
├── philosophy/ ← 00–12
├── pcb/ ← 00–13, plus documents/ (Dave Jones' 2004 PCB design tutorial)
├── btc/ ← 00–13
├── software/ ← 00–13
├── templates/ ← note and project templates
├── my/ ← YOUR progress, notes and projects (created by tools/progress.py; git ignores it)
├── reviews/ ← dated audit reports, e.g. 2026-10-deep-dive/
├── tools/ ← progress.py (learners), build.py and check_repo.py (maintainers); see tools/README.md
├── .github/workflows/ ← checks on every push, and a monthly link check
├── .agents/skills/ ← update-curriculum and audit-curriculum skills for AI agents
├── LICENSE ← CC BY 4.0 (tools/ is MIT)
└── CONTRIBUTING.md ← how to add or fix a resource
Some sections were added after the original numbering. They keep their numbers, so existing links and NotebookLM sources still work; each curriculum's learning path puts them in the right place.
- Resources are data. Each section's resources live in its
resources.toml, with averifieddate for the last time someone opened the link and checked it.python tools/build.pyregenerates the tables, the PROGRESS templates, the book and course indexes and the learning paths. See tools/README.md. - Checks:
python tools/check_repo.pyruns offline and checks links between files, section anatomy, tier lists, the data, and that nothing generated is stale. GitHub runs it on every push and pull request, and checks every external link once a month, opening an issue if any broke. - Adding material: see CONTRIBUTING.md, or have an AI agent use the
update-curriculumskill. Every link is opened before it is added, and people are cited rather than organizations. - Audits: the
audit-curriculumskill runs about once a quarter. Reports go inreviews/. The October 2026 deep dive covers what was wrong, what changed, and why.
- Curricula, guides and data: CC BY 4.0. Share and adapt them, with credit.
- Code in
tools/: MIT. - Linked resources belong to their authors and publishers. The one bundled document, Dave Jones' PCB design tutorial, keeps its own terms (see pcb/documents).