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Artificial intelligence : with an introduction to machine learning /

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The first edition of this popular textbook, Contemporary Artificial Intelligence, provided an accessible and student friendly introduction to AI. This fully revised and expanded update, Artificial Intelligence: With an Introduction to Machine Learning, Second Edition, retains the same accessibility...

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Bibliographic Details
Main Authors: Neapolitan, Richard E. (Author), Jiang, Xia, 1967- (Author)
Corporate Author: Taylor & Francis
Format: eBook
Language:English
Published: Boca Raton, FL : CRC Press, Taylor & Francis Group, [2018]
Edition:Second edition.
Series:Chapman & Hall/CRC artificial intelligence and robotics series.
Subjects:
Artificial intelligence.
Artificial Intelligence
Intelligence artificielle.
artificial intelligence.
COMPUTERS > General.
Artificial intelligence
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Intro
  • Title Page
  • Copyright Page
  • Table of Contents
  • Preface
  • About the Authors
  • 1 Introduction to Artificial Intelligence
  • 1.1 History of Artificial Intelligence
  • 1.1.1 What Is Artificial Intelligence?
  • 1.1.2 Emergence of AI
  • 1.1.3 Cognitive Science and AI
  • 1.1.4 Logical Approach to AI
  • 1.1.5 Knowledge-Based Systems
  • 1.1.6 Probabilistic Approach to AI
  • 1.1.7 Evolutionary Computation and Swarm Intelligence
  • 1.1.8 Neural Networks & Deep Learning
  • 1.1.9 A Return to Creating HAL
  • 1.2 Outline of This Book
  • PART I Logical Intelligence
  • 2 Propositional logic
  • 2.1 Basics of propositional logic
  • 2.1.1 Syntax
  • 2.1.2 Semantics
  • 2.1.3 Tautologies and logical implication
  • 2.1.4 Logical arguments
  • 2.1.5 Derivation systems
  • 2.2 Resolution
  • 2.2.1 Normal forms
  • 2.2.2 Derivations using resolution
  • 2.2.3 Resolution algorithm
  • 2.3 Artificial intelligence applications
  • 2.3.1 Knowledge-based systems
  • 2.3.2 Wumpus world
  • 2.4 Discussion and further reading
  • 3 First-order logic
  • 3.1 Basics of first-order logic
  • 3.1.1 Syntax
  • 3.1.2 Semantics
  • 3.1.3 Validity and Logical Implication
  • 3.1.4 Derivation Systems
  • 3.1.5 Modus Ponens for First-Order Logic
  • 3.2 Artificial Intelligence Applications
  • 3.2.1 Wumpus World Revisited
  • 3.2.2 Planning
  • 3.3 Discussion and Further Reading
  • 4 Certain knowledge representation
  • 4.1 Taxonomic Knowledge
  • 4.1.1 Semantic Nets
  • 4.1.2 Model of Human Organization of Knowledge
  • 4.2 Frames
  • 4.2.1 Frame Data Structure
  • 4.2.2 Planning a Trip Using Frames
  • 4.3 Nonmonotonic Logic
  • 4.3.1 Circumscription
  • 4.3.2 Default Logic
  • 4.3.3 Difficulties
  • 4.4 Discussion and Further Reading
  • 5 Learning deterministic models
  • 5.1 Supervised Learning
  • 5.2 Regression
  • 5.2.1 Simple Linear Regression
  • 5.2.2 Multiple Linear Regression.
  • 15 Neural Networks and Deep Learning
  • 15.1 The Perceptron
  • 15.1.1 Learning the Weights for a Perceptron
  • 15.1.2 The Perceptron and Logistic Regression
  • 15.2 Feedforward Neural Networks
  • 15.2.1 Modeling XOR
  • 15.2.2 Example with Two Hidden Layers
  • 15.2.3 Structure of a Feedforward Neural Network
  • 15.3 Activation Functions
  • 15.3.1 Output Nodes
  • 15.3.2 Output Nodes
  • 15.4 Application to Image Recognition
  • 15.5 Discussion and Further Reading
  • PART V Language Understanding
  • 16 Natural Language Understanding
  • 16.1 Parsing
  • 16.1.1 Recursive Parser
  • 16.1.2 Ambiguity
  • 16.1.3 Dynamic Programming Parser
  • 16.1.4 Probabilistic Parser
  • 16.1.5 Obtaining Probabilities for a PCFG
  • 16.1.6 Lexicalized PCFG
  • 16.2 Semantic Interpretation
  • 16.3 Concept/Knowledge Interpretation
  • 16.4 Information Extraction
  • 16.4.1 Applications of Information Extraction
  • 16.4.2 Architecture for an Information Extraction System
  • 16.5 Discussion and Further Reading
  • References
  • Index.