Home > Store

Artificial Intelligence: A Modern Approach, 4th Edition

Register your product to gain access to bonus material or receive a coupon.

Artificial Intelligence: A Modern Approach, 4th Edition

Book

  • Your Price: $209.65
  • List Price: $246.65
  • Usually ships in 24 hours.

About

Features

Hallmark features of this title

  • Nontechnical learning material introduces major concepts using intuitive explanations, before going into mathematical or algorithmic details. The nontechnical language makes this book accessible to a broader range of readers.
  • A unified approach to AI clearly details how the various subfields of AI fit together to build actual, useful programs.
  • In-depth coverage of both basic and advanced topics provides students with a solid understanding of the frontiers of AI without compromising complexity and depth.
  • The author-maintained website at http://aima.cs.berkeley.edu/ includes video tutorials, interactive student exercises, and supplemental coding examples and applications in Python, Java and Javascript.

Description

  • Copyright 2021
  • Dimensions: 8" x 10"
  • Pages: 1136
  • Edition: 4th
  • Book
  • ISBN-10: 0-13-461099-7
  • ISBN-13: 978-0-13-461099-3

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.

Sample Content

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

Updates

Submit Errata

More Information

InformIT Promotional Mailings & Special Offers

I would like to receive exclusive offers and hear about products from InformIT and its family of brands. I can unsubscribe at any time.