The textbooks, courses, and papers behind the CS1–CS8 mid-sem material — what to read for depth, and where each topic comes from. The course textbook (Russell & Norvig) covers nearly everything; the rest fill in the computational-intelligence and neural-architecture-search pieces.
Textbooks
- T1Stuart Russell & Peter Norvig — Artificial Intelligence: A Modern Approach, 4th ed., Pearson, 2022. Course textbook. CS1–CS2 → Ch. 1–2 (agents); CS3–CS4 → Ch. 3 (search) & Ch. 4 (local search); CS5 → Ch. 4 (GA) & Ch. 4.5 (online search); CS7–CS8 → Ch. 5 (adversarial search, minimax, alpha-beta, MCTS).
- R1Andries P. Engelbrecht — Computational Intelligence: An Introduction, 2nd ed., Wiley. The CI half of the course — genetic algorithms, swarm intelligence (ACO/PSO), and neural systems.
- R3Elaine Rich, Kevin Knight & Shivashankar B. Nair — Artificial Intelligence, 3rd ed., Tata McGraw Hill. Alternative treatment of search and game playing.
Courses & lectures
- C1UC Berkeley CS188 — Introduction to Artificial Intelligence. The classic free course; lectures and the Pacman projects map directly onto CS3–CS8 (search, A*, adversarial search, expectimax, MCTS). inst.eecs.berkeley.edu/~cs188
- C2BITS Pilani WILP — Artificial & Computational Intelligence (AIMLCZG557). Source lecture decks (CS1–CS8). Slide prep/review: Prof. Rajavadhana, Prof. Indumathi, Prof. Sangeetha, Prof. Parthasarathy PD, Prof. Ramya Devi; Mr. Santosh GSK.
Papers (Neural Architecture Search — CS6)
- P1Stanley & Miikkulainen (2002) — "Evolving Neural Networks through Augmenting Topologies" (NEAT). Evolutionary Computation 10(2). Origin of innovation numbers + speciation.
- P2Miikkulainen et al. (2017) — "Evolving Deep Neural Networks" (DeepNEAT & CoDeepNEAT). arXiv:1703.00548. The blueprint + module co-evolution used in CS6.
- P3Dorigo, Maniezzo & Colorni (1996) — "Ant System: Optimization by a Colony of Cooperating Agents." IEEE Trans. SMC-B 26(1). The original ACO / pheromone model behind CS5–CS6.
- P4Silver et al. (2016) — "Mastering the Game of Go with Deep Neural Networks and Tree Search" (AlphaGo). Nature 529. MCTS + deep RL, the modern face of CS8.
Extra reads
- E1AIMA companion site — figures, pseudocode, and code for every algorithm in the textbook. aima.cs.berkeley.edu
- E2Cheatsheet, slides explained & question bank — the rest of this vault: the cheatsheet, the slides explained, the formula sheet, and the question bank.