Books and documentation
The chapter notes develop the module’s main material. These books and documentation pages provide alternative explanations, fuller proofs, and the tools used for larger problems. The reading plan lists the lecture slides and readings for each topic.
Main references
| Source | Relevant material |
|---|---|
| Russell and Norvig, Artificial Intelligence: A Modern Approach, fourth edition (Pearson, 2020) | The standard textbook: chapters 2–3 (chapter 1), 3.5–4 (chapters 2–3), 5 (chapter 4), 6 (chapter 3), 7 (chapter 5), 8–10 (chapter 6), 11 (chapter 7), 12–13 (chapters 8–10), 14 (chapter 11), 16 (chapter 12), 18 (chapter 15), and 20 (chapter 14). Not freely available; the book page links code and exercises. |
| Poole and Mackworth, Artificial Intelligence: Foundations of Computational Agents, third edition (Cambridge, 2023) | Free HTML: chapters 1 and 3 (chapters 1–2), 4 (chapter 3), 5 (chapter 5), 6 (chapter 7), 9–10 (chapters 8–11 and 14), 11 (chapter 13), 12 (chapter 12), 14 (chapter 15), and 15–16 (chapter 6). |
| CS188 online textbook | The main course’s notes: search, CSPs, games, Bayesian networks, decision networks, HMMs, and logic (chapters 1–6 and 8–12). |
| Koller and Friedman, Probabilistic Graphical Models: Principles and Techniques (MIT Press, 2009) | The comprehensive reference for chapters 8–11 and 14: representation (3–6), exact inference (9–10, 13), approximate inference (11–12), temporal models (6, 15), and learning (16–20). Not freely available. |
Specialized books and monographs
| Source | Relevant material |
|---|---|
| Wainwright and Jordan, Graphical Models, Exponential Families, and Variational Inference (2008) | The variational view of inference: mean field, Bethe and loopy belief propagation, and convex relaxations (chapters 9–10 and 14). |
| Murphy, Probabilistic Machine Learning: Advanced Topics (MIT Press, 2023) | Free PDF; graphical models, inference algorithms, Monte Carlo, variational inference, state-space models, and causality (chapters 8–11, 13–14). |
| Ghallab, Nau, and Traverso, Automated Planning and Acting (Cambridge, 2016) | Planning with deterministic and nondeterministic models, hierarchical refinement, and acting (chapter 7). |
| Thrun, Burgard, and Fox, Probabilistic Robotics (MIT Press, 2005) | Bayes filters, Kalman and particle filters, and localization (chapter 11). |
| Pearl, Causality, second edition (Cambridge, 2009) | Structural causal models, do-calculus, and counterfactuals (chapter 13). |
| Peters, Janzing, and Schölkopf, Elements of Causal Inference (MIT Press, 2017) | Open access; causal models, identifiability from observational data, and learning (chapter 13). |
| Neal, Introduction to Causal Inference (course book, 2020) | Free PDF of the causal-inference course used in chapter 13. |
| Hernán and Robins, Causal Inference: What If (2020, revised) | The potential-outcomes view with epidemiological examples: confounding, selection bias, and estimation (chapter 13). |
| Shoham and Leyton-Brown, Multiagent Systems (Cambridge, 2009) | Free PDF; normal-form and extensive-form games, computing equilibria, learning in games, mechanism design, and social choice (chapter 15). |
| Nisan, Roughgarden, Tardos, and Vazirani, eds., Algorithmic Game Theory (Cambridge, 2007) | Complexity of equilibria, no-regret learning, the price of anarchy, and auctions (chapter 15). |
Library documentation
| Source | Relevant material |
|---|---|
| NumPy documentation | Arrays, einsum for factor products, and random generation; used in every chapter. |
SciPy linprog | Linear programming for zero-sum games (chapter 15). |
| NetworkX documentation | Grid graphs and maximum spanning trees (chapters 9 and 14). |
Tools for larger problems
The notes implement every algorithm from scratch on small examples. For real problems, mature implementations exist:
| Tool | Use |
|---|---|
| PySAT | Python bindings to modern CDCL SAT solvers (chapter 5). |
| Fast Downward | A classical planning system implementing the heuristics of chapter 7 on PDDL tasks. |
| pgmpy | Bayesian and Markov networks: exact and approximate inference, parameter and structure learning (chapters 8–10, 14). |
| Stan and PyMC | Probabilistic programming with Hamiltonian Monte Carlo and variational inference (chapter 10). |
| DoWhy | Causal effect identification, estimation, and refutation tests (chapter 13). |
| OpenSpiel | Games and algorithms, including MCTS, CFR, and regret matching (chapters 4 and 15). |