Course links
| Course | Links | Role |
|---|---|---|
| UC Berkeley CS188, Spring 2024 — Introduction to Artificial Intelligence | Course · Online textbook · Lecture playlist · Projects | Main course: agents, search, games, propositional and first-order logic, Bayesian networks, hidden Markov models, particle filtering, and decision networks (chapters 1–2, 4–6, 8–12). Its Pacman projects implement search, adversarial search, logic, and probabilistic tracking with autograders. |
| UC Berkeley CS188, Spring 2023 | Course | The two lectures on constraint satisfaction, which Spring 2024 omits (chapter 3). |
| CMU 10-708, Spring 2020 — Eric Xing, Probabilistic Graphical Models | Lectures with slides, scribe notes, and videos | Supplement at graduate depth: undirected models, exact and approximate inference, parameter and structure learning, and causality (chapters 8–11, 13–14). |
| Stanford CS228 — Stefano Ermon, Probabilistic Graphical Models | Course notes | Written notes on representation, inference, and learning (chapters 8–10 and 14). |
| Stanford CS221, Autumn 2019 — Percy Liang and Dorsa Sadigh, Artificial Intelligence: Principles and Techniques | Course | Selected lectures on factor graphs and logic (chapters 3, 5, 6, and 8). |
| University of Basel, Fall 2023 — Malte Helmert and Gabriele Röger, Planning and Optimization | Course page with slides | Classical planning in depth: STRIPS, complexity, SAT planning, delete-relaxation heuristics, abstractions, and landmarks (chapter 7). |
| Brady Neal, Introduction to Causal Inference | Course page · Course book · PDF | Potential outcomes, graphs, identification, estimation, instruments, discovery, and counterfactuals (chapter 13). |
| Stanford CS364A, Fall 2013 — Tim Roughgarden, Algorithmic Game Theory | Course page with lecture notes | Equilibrium concepts, no-regret dynamics, the price of anarchy, and mechanism design (chapter 15). |
Two collections of code complement the courses:
| Resource | Link | Role |
|---|---|---|
| AIMA Python | Repository | Implementations of most algorithms of Russell and Norvig’s textbook, with notebooks. |
| CS188 Pacman projects | Projects | Search, multiagent search, logic, and Bayesian-network tracking in a Pacman world, with autograders; the machine-learning and reinforcement-learning projects belong with ML, DL, and RL. |
The reading plan maps individual lectures, slides, and readings to the study sequence. Video links arranges the recordings by chapter, and the AI overview maps the reading-plan topics to the chapters.