Artificial Intelligence: A Modern Approach (aima.cs.berkeley.edu)

Stuart Russell and Peter Norvig’s Artificial Intelligence: A Modern Approach (4th US edition, updated Aug 22, 2022) remains the definitive, classroom-tested AI textbook—adopted by over 1,500 schools—and has been expanded to reflect contemporary advances across AI/ML. The edition presents a coherent, unified curriculum spanning foundational agent-based views, classical search and planning, knowledge representation and logical inference, through probabilistic reasoning, decision theory, and modern machine learning. Its continued prominence makes it a common reference for researchers, educators, and practitioners seeking both breadth and rigor. Technically, the book maps the full AI stack: search and adversarial algorithms; first-order logic and inference; probabilistic models, reasoning over time, and probabilistic programming; decision making including multiagent systems; and a comprehensive machine learning section covering learning from examples, probabilistic models, deep learning, and reinforcement learning. The perceptual and applied sections include NLP (and deep NLP), computer vision, and robotics, alongside practical resources—code, exercises, pseudocode, and appendices on mathematics and algorithms. Notably, chapters on ethics, safety, and the future of AI situate technical material within societal context, underscoring the book’s role in shaping both technical training and responsible deployment of AI systems.