Self-study notes for a solid foundation in AI, inspired by UNSW COMP9814 Extended Artificial Intelligence (24T3).
Read it here → https://in-c0.github.io/Intro-to-AI/
| Chapter | |
|---|---|
| 1 | Introduction — history of AI, agents, knowledge representation |
| 2 | Search — BFS, DFS, iterative deepening, informed search |
| 3 | Artificial Neural Networks — perceptrons, MLPs, network design |
| 4 | Reinforcement Learning — action values, returns, value functions, MC & TD |
| 5 | Metaheuristics — gradient methods, simulated annealing, tabu search, GAs |
Projects: Mountain Car with Gym · Feedforward Neural Network
pip install -r requirements.txt
mkdocs serve # http://127.0.0.1:8000/Intro-to-AI/
mkdocs build --strict # same check CI runs; fails on broken links/anchorsFixes and new sections are welcome — open an issue or a pull request. Every PR is link-checked automatically.