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 # Graphics
 
 - [Shader Programming with wgpu and WGSL](shaders.md)
+
+# Machine Learning
+
+- [Training a Game AI Through Self-Play](ml-self-play.md)
diff --git a/edu/src/ml-self-play.md b/edu/src/ml-self-play.md
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+# Machine Learning: Training a Game AI Through Self-Play
+
+This is a self-guided course on reinforcement learning through the lens of a concrete goal: training a Rust program to play Tic-Tac-Toe by playing against itself, in the style of AlphaGo Zero. No prior ML experience is assumed. You will build everything from scratch — a game engine, a search algorithm, and eventually a neural network that guides that search. Sections marked 🚧 are stubs whose full content is tracked in a beans ticket.
+
+---
+
+## Table of Contents
+
+**Part 1 — Foundations**
+
+1. [What is reinforcement learning?](#1-what-is-reinforcement-learning)
+2. [Monte Carlo Tree Search — algorithm explained](#2-monte-carlo-tree-search--algorithm-explained)
+3. [Why self-play? The AlphaGo Zero insight](#3-why-self-play-the-alphago-zero-insight)
+
+**Part 2 — The Game**
+
+4. [Choosing a simple game: Tic-Tac-Toe](#4-choosing-a-simple-game-tic-tac-toe)
+5. [Representing game state in Rust](#5-representing-game-state-in-rust)
+6. [Exercise 1: implement the game logic](#6-exercise-1-implement-the-game-logic)
+
+**Part 3 — MCTS**
+
+7. [Implementing MCTS in Rust](#7-implementing-mcts-in-rust)
+8. [Exercise 2: play Tic-Tac-Toe with pure MCTS](#8-exercise-2-play-tic-tac-toe-with-pure-mcts)
+
+**Part 4 — Neural Network Policy/Value Head**
+
+9. [Neural network architecture overview](#9-neural-network-architecture-overview)
+10. [Integrating a neural network crate](#10-integrating-a-neural-network-crate)
+11. [Exercise 3: train the network on MCTS data](#11-exercise-3-train-the-network-on-mcts-data)
+12. [Exercise 4: replace rollout with the value network](#12-exercise-4-replace-rollout-with-the-value-network)
+
+**Part 5 — Self-Play Loop**
+
+13. [The full AlphaGo Zero training loop](#13-the-full-alphago-zero-training-loop)
+14. [Exercise 5: 1000 self-play games; observe improvement](#14-exercise-5-1000-self-play-games-observe-improvement)
+
+---
+
+## Part 1 — Foundations
+
+### 1. What is reinforcement learning?
+
+🚧 This section is a stub. Full content tracked in [edu-wobk].
+
+---
+
+### 2. Monte Carlo Tree Search — algorithm explained
+
+🚧 This section is a stub. Full content tracked in [edu-3yw9].
+
+---
+
+### 3. Why self-play? The AlphaGo Zero insight
+
+🚧 This section is a stub. Full content tracked in [edu-5go8].
+
+---
+
+## Part 2 — The Game
+
+### 4. Choosing a simple game: Tic-Tac-Toe
+
+🚧 This section is a stub. Full content tracked in [edu-k3tq].
+
+---
+
+### 5. Representing game state in Rust
+
+🚧 This section is a stub. Full content tracked in [edu-e39n].
+
+---
+
+### 6. Exercise 1: implement the game logic
+
+🚧 This section is a stub. Full content tracked in [edu-ymux].
+
+---
+
+## Part 3 — MCTS
+
+### 7. Implementing MCTS in Rust
+
+🚧 This section is a stub. Full content tracked in [edu-of9y].
+
+---
+
+### 8. Exercise 2: play Tic-Tac-Toe with pure MCTS
+
+🚧 This section is a stub. Full content tracked in [edu-4v13].
+
+---
+
+## Part 4 — Neural Network Policy/Value Head
+
+### 9. Neural network architecture overview
+
+🚧 This section is a stub. Full content tracked in [edu-iv0k].
+
+---
+
+### 10. Integrating a neural network crate
+
+🚧 This section is a stub. Full content tracked in [edu-pvou].
+
+---
+
+### 11. Exercise 3: train the network on MCTS data
+
+🚧 This section is a stub. Full content tracked in [edu-lqky].
+
+---
+
+### 12. Exercise 4: replace rollout with the value network
+
+🚧 This section is a stub. Full content tracked in [edu-7lu6].
+
+---
+
+## Part 5 — Self-Play Loop
+
+### 13. The full AlphaGo Zero training loop
+
+🚧 This section is a stub. Full content tracked in [edu-453h].
+
+---
+
+### 14. Exercise 5: 1000 self-play games; observe improvement
+
+🚧 This section is a stub. Full content tracked in [edu-brtk].