Artificial Intelligence: A Modern Approach
About the Book:
Authors: Stuart J. Russell (UC Berkeley), Peter Norvig (Google Research)
Editions: First published in 1995; 4th edition (Global, US) released in 2020 and updated in 2024.
Status: Most widely used and cited AI textbook; adopted by over 1500 universities globally
Audience: Undergraduate and graduate students, AI developers, research professionals
Key Highlights:
Comprehensive Coverage: Explores both classical foundations and current advances in AI (logic, probability, robotics, deep learning, NLP, ethics)
Unified Framework: Synthesizes disparate subfields of AI into a single, modern approach
Algorithm Focus: In-depth walkthrough of search, reasoning, planning, learning, vision, and language algorithms (pseudo-code and actual code in multiple languages)
Latest Technologies: Machine learning, deep learning, reinforcement learning, multi-agent systems, probabilistic programming, causality, ethics, fairness, AI safety, privacy
Practical Applications: Real-world AI systems (autonomous vehicles, robotics, speech recognition, translation, online services)
Companion Resources: Online code repository (Java, Python, Lisp, JavaScript, Scala), online exercises, figures, and resources
Ethics & Future: Dedicated chapters on philosophy, safety, ethics, risks, and the future impact of AI
| Section | Topics Covered |
|---|---|
| Introduction & Foundations | Defining AI, history, foundations, state-of-the-art, risks & benefits |
| Intelligent Agents | Agents & environments, rationality, agent structures |
| Problem Solving & Search | Uninformed/informed (heuristic) search, optimization, constraint satisfaction |
| Knowledge, Reasoning & Planning | Logic, inference, knowledge representation, automated planning |
| Uncertainty & Decisions | Probabilistic reasoning, decision-making under uncertainty, probabilistic programming |
| Machine Learning | Supervised/unsupervised learning, deep learning, reinforcement learning, transfer learning |
| Perception & Action | Natural language processing, deep NLP, computer vision, robotics |
| Ethics & Future of AI | Philosophy, ethics, safety, social impact, the future of AI |
Why Read This Book?
Depth & Breadth: Covers every aspect of AI from the mathematical foundation, problem-solving methods, logic, learning algorithms, up to deep learning and real-world applications.
Standard Textbook: Industry gold standard for teaching and reference; used by top universities and technology companies.
Accessible: Mathematical content is self-contained; examples and exercises suitable for CS, EE, and related fields.
Forward-Looking: Discusses not just technical topics, but societal, safety, and ethical challenges of modern AI.
