Artificial Intelligence: A Modern Approach, 4th edition

  • Stuart Russell
  • , Peter Norvig

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ISBN-13: 9780137505135 (2021 update)

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ISBN-13: 9780137505135 (2021 update)

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Artificial Intelligence: A Modern Approach, 4th Edition, by Stuart Russell and Peter Norvig, is the definitive AI textbook. Used by more than 1,500 universities, it introduces major concepts using intuitive explanations and nontechnical language, before going into mathematical or algorithmic details. AI ethics and safety are integrated throughout, emphasizing responsible AI use.

With this trusted textbook, you’ll build a strong foundation in modern AI concepts, from search and optimization to constraint satisfaction, adversarial games, intelligent agents, planning, and logic and knowledge representation. You’ll then apply mathematical thinking as you explore machine learning, natural language processing, robotics, deep learning, probabilistic reasoning, and Bayesian networks.

The 4th Edition explores the latest technologies while presenting concepts more cohesively. New chapters expand on probabilistic programming, multiagent decision-making, deep learning, and deep learning for natural language processing. Revised coverage of computer vision, speech recognition, and natural language understanding reflects the growing impact of deep learning on these rapidly evolving fields.

Published by Pearson (December 21st 2021) - Copyright © 2022

ISBN-13: 9780137505135

Subject: Artificial Intelligence

Category: Introduction to Artificial Intelligence

  1. Introduction
  2. Intelligent Agents
  3. Solving Problems by Searching
  4. Search in Complex Environments
  5. Adversarial Search and Games
  6. Constraint Satisfaction Problems
  7. Logical Agents
  8. First-Order Logic
  9. Inference in First-Order Logic
  10. Knowledge Representation
  11. Automated Planning
  12. Quantifying Uncertainty
  13. Probabilistic Reasoning
  14. Probabilistic Reasoning over Time
  15. Probabilistic Programming
  16. Making Simple Decisions
  17. Making Complex Decisions
  18. Multiagent Decision Making
  19. Learning from Examples
  20. Learning Probabilistic Models
  21. Deep Learning
  22. Reinforcement Learning
  23. Natural Language Processing
  24. Deep Learning for Natural Language Processing
  25. Robotics
  26. Philosophy and Ethics of AI
  27. The Future of AI