Artificial Intelligence : With an Introduction to Machine Learning, Second Edition / by Richard E. Neapolitan and Xia Jiang.

Format:

Book

Author/Creator:

Neapolitan, Richard E., author.

Jiang, Xia, author.

Series:

Chapman & Hall/CRC Artificial Intelligence and Robotics Series.

Chapman & Hall/CRC Artificial Intelligence and Robotics Series

A Chapman & Hall Book

Language:

English

Subjects (All):

Artificial intelligence.

Physical Description:

1 recurso en línea (467 p.) il.

Edition:

2nd ed.

Place of Publication:

Boca Raton, FL : Chapman and Hall/CRC, 2018.

Summary:

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 and problem-solving approach, while providing new material and methods. The book is divided into five sections that focus on the most useful techniques that have emerged from AI. The first section of the book covers logic-based methods, while the second section focuses on probability-based methods. Emergent intelligence is featured in the third section and explores evolutionary computation and methods based on swarm intelligence. The newest section comes next and provides a detailed overview of neural networks and deep learning. The final section of the book focuses on natural language understanding. Suitable for undergraduate and beginning graduate students, this class-tested textbook provides students and other readers with key AI methods and algorithms for solving challenging problems involving systems that behave intelligently in specialized domains such as medical and software diagnostics, financial decision making, speech and text recognition, genetic analysis, and more.

Contents:

1. Introduction to Artificial Intelligence

1.1 History of Artificial Intelligence

1.2 Outline of this Book

Part I LOGICAL INTELLIGENCE

2. Propositional Logic

2.1 Basics of Propositional Logic

2.2 Resolution

2.3 Artificial Intelligence Applications

2.4 Discussion and Further Reading

3. First-Order Logic

3.1 Basics of First-Order Logic

3.2 Artificial Intelligence Applications

3.3 Discussion and Further Reading

4. Certain Knowledge Representation

4.1 Taxonomic Knowledge

4.2 Frames

4.3 Nonmonotonic Logic

4.4 Discussion and Further Reading

5. Learning Deterministic Models

5.1 Supervised Learning

5.2 Regression

5.3 Parameter Estimation

5.4 Learning a Decision Tree

PART II PROBABILISTIC INTELLIGENCE

6. Probability

6.1 Probability Basics

6.2 RandomVariables

6.3 Meaning of Probability

6.4 RandomVariables in Applications

6.5 Probability in the Wumpus World

7. Uncertain Knowledge Representation

7.1 Intuitive Introduction to Bayesian Networks

7.2 Properties of Bayesian Networks

7.3 Causal Networks as Bayesian Networks

7.4 Inference in Bayesian Networks

7.5 Networks with Continuous Variables

7.6 Obtaining the Probabilities

7.7 Large-Scale Application: Promedas

8. Advanced Properties of Bayesian Network

8.1 Entailed Conditional Independencies

8.2 Faithfulness

8.3 Markov Equivalence

8.4 Markov Blankets and Boundaries

9. Decision Analysis

9.1 Decision Trees

9.2 Influence Diagrams

9.3 Modeling Risk Preferences

9.4 Analyzing Risk Directly

9.5 Good Decision versus Good Outcome

9.6 Sensitivity Analysis

9.7 Value of Information

9.8 Discussion and Further Reading

10. Learning Probabilistic Model Parameters

10.1 Learning a Single Parameter

10.2 Learning Parameters in a Bayesian Network .

10.3 Learning Parameters with Missing Data

11. Learning Probabilistic Model Structure

11.1 Structure Learning Problem

11.2 Score-Based Structure Learning

11.3 Constraint-Based Structure Learning

11.4 Application: MENTOR

11.5 Software Packages for Learning

11.6 Causal Learning

11.7 Class Probability Trees

11.8 Discussion and Further Reading

12. Unsupervised Learning and Reinforcement Learning

12.1 Unsupervised Learning

12.2 Reinforcement Learning

12.3 Discussion and Further Reading

PART III EMERGENT INTELLIGENCE

13. Evolutionary Computation

13.1 Genetics Review

13.2 Genetic Algorithms

13.3 Genetic Programming

13.4 Discussion and Further Reading

14. Swarm Intelligence

14.1 Ant System

14.2 Flocks

14.3 Discussion and Further Reading

PART IV NEURAL INTELLIGENCE

15. Neural Networks and Deep Learning

15.1 The Perceptron

15.2 Feedforward Neural Networks

15.3 Activation Functions

15.4 Application to Image Recognition

15.5 Discussion and Further Reading

PART V LANGUAGE UNDERSTANDING

16. Natural Language Understanding

16.1 Parsing

16.2 Semantic Interpretation

16.3 Concept/Knowledge Interpretation

16.4 Information Extraction

16.5 Discussion and Further Reading.

Notes:

7.4 Inference in Bayesian Networks.

Incluye bibliografía e índice

ISBN:

1-351-38439-2

1-315-14486-7

1-351-38438-4

9781315144863

OCLC:

1029252080