Pearson+

Artificial Intelligence: A Modern Approach, 4th edition

  • Stuart Russell
  • , Peter Norvig

  • Listen on the go
    Listen on the go

    Learn how you like with full eTextbook audio

  • Find it fast
    Find it fast

    Quickly navigate your eTextbook with search

  • Stay organized
    Stay organized

    Access all your eTextbooks in one place

  • Easily continue access
    Easily continue access

    Keep learning with auto-renew

Artificial Intelligence is your guide to the theory and practice of modern AI. It introduces major concepts using intuitive explanations and nontechnical language, before going into mathematical or algorithmic details. In-depth coverage of both basic and advanced topics provides you with a solid understanding of the frontiers of AI without compromising complexity and depth. A unified approach to AI clearly details how the various subfields of AI fit together to build actual, useful programs.

The 4th Edition has been updated to stay current with the latest technologies as well as to present concepts in a more unified manner. New chapters feature expanded coverage of probabilistic programming, multiagent decision making, deep learning and deep learning for natural language processing. Revised coverage of computer vision, natural language understanding and speech recognition reflect the impact of deep learning methods on these fields.

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

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