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

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

  • Length: 480 pages
  • Edition: 2
  • Publisher:
  • Publication Date: 2018-05-16
  • ISBN-10: 1138502383
  • ISBN-13: 9781138502383
  • Sales Rank: #1143341 (See Top 100 Books)
Description

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.

Table of Contents

Chapter 1 Introduction to Arti cial 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 II 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 III Emergent Intelligence
Chapter 13 Evolutionary Computation
Chapter 14 Swarm Intelligence

Part IV Neural Intelligence
Chapter 15 Neural Networks and Deep Learning

Part V Language Understanding
Chapter 16 Natural Language Understanding

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