Artificial Intelligence: A Modern Approach eBook, 4th Edition

By Stuart Russell, Peter Norvig

Title type
eBook
$60.00
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  • This eBook can only be purchased by people residing in Australia.
     
     
     

    Description
    The most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence

    The long-anticipated revision of Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up to date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multiagent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.

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    Features
    • Nontechnical learning material introduces major concepts using intuitive explanations, before going into mathematical or algorithmic details. The nontechnical language makes the book accessible to a broader range of readers.
    • A unified approach to AI shows students how the various subfields of AI fit together to build actual, useful programs.
    • The basic definition of AI systems is generalised to eliminate the standard assumption that the objective is fixed and known by the intelligent agent; instead, the agent may be uncertain about the true objectives of the human(s) on whose behalf it operates.
    • In-depth coverage of both basic and advanced topics provides students with a basic understanding of the frontiers of AI without compromising complexity and depth.
    • Stay current with the latest technologies and present concepts in a more unified manner
    • New chapters feature expanded coverage of probabilistic programming (Ch. 15); multiagent decision making (Ch. 18 with Michael Wooldridge); deep learning (Ch. 21 with Ian Goodfellow); and deep learning for natural language processing (Ch. 24 with Jacob Devlin and Mei-Wing Chang).
    • Increased coverage of machine learning.
    • Significantly updated material on robotics includes robots that interact with humans and the application of reinforcement learning to robotics.
    • New section on causality by Judea Pearl.
    • New sections on Monte Carlo search for games and robotics.
    • New sections on transfer learning for deep learning in general and for natural language.
    • New sections on privacy, fairness, the future of work, and safe AI.
    • Extensive coverage of recent advances in AI applications.
    • Revised coverage of computer vision, natural language understanding, and speech recognition reflect the impact of deep learning methods on these fields.
    Product details
    ISBN
     
    9780134671932
    Edition
     
    4th
    Published date
     
    08/07/2020
    Published by
     
    Pearson Higher Ed USA
    Pages
     
    Format
     
    Table of contents
    • 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