Artificial Intelligence: With an Introduction to Machine Learning, 2nd Edition

Artificial Intelligence: With an Introduction to Machine Learning, Second Edition (Chapman & Hall/CRC Artificial Intelligence and Robotics Series)9781138502383

Artificial Intelligence: With an Introduction to Machine Learning, Second Edition (Chapman & Hall/CRC Artificial Intelligence and Robotics Series)

By 作者: Richard E. Neapolitan - Xia Jiang
ISBN-10 书号: 1138502383
ISBN-13 书号: 9781138502383
Edition 版本: 2
Release Finelybook 出版日期: 2018-05-16
Pages 页数: 480
5

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


Preface
About the Authors
Chapter
1 Introduction to Artincial Intelligence
Part
I Logical Intelligence
Chapter
2 Propositional Logic
Chapter
3 First-Order Logic
Chapter
4 Certain Knowledge Representation
Chapter
5 Learning Deterministic Models
Part
ll Probabilistic Intelligence
Chapter
6 Probability
Chapter
7 Uncertain Knowledge Representation
Chapter
8 Advanced Properties of Bayesian Networks
Chapter
9 Decision Analysis
Chapter
10 Learning Probabilistic Model Parameters
Chapter
11 Learning Probabilistic Model Structure
Chapter
12 Unsupervised Learning and Reinforcement Learning
Part
ll Emergent Intelligence
Chapter
13 Evolutionary Computation
Chapter
14 Swarm Intelligence
Part
IV Neural Intelligence
Chapter
15Neural Networks and Deep Learning
Part
V Language Understanding
Chapter
16 Natural Language Understanding
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