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Artificial intelligence : with an introduction to machine learning / by Richard E. Neapolitan , Xia Jiang

By: Contributor(s): Material type: TextTextSeries: Chapman & Hall/CRC artificial intelligence and robotics seriesPublication details: Boca Raton, FL: CRC Press, Taylor & Francis Group, 2018Edition: 2nd edDescription: 446 p. : figs, ; 23 cmISBN:
  • 9781138502383
Subject(s): DDC classification:
  • 006.3  NEA
Contents:
1. Introduction to Artificial Intelligence
Part 1: Logical Intelligence
2. Propositional Logic
3. First-Order Logic
4. Certain Knowledge Representation
5. Learning Deterministic Models
Part 2: Probabilistic Intelligence
6. Probability
7. Uncertain Knowledge Representation
8. Advanced Properties of Bayesian Network
9. Decision Analysis
10. Learning Probabilistic Model Parameters
11. Learning Probabilistic Model Structure
12. Unsupervised Learning and Reinforcement Learning
Part 3: Emergent Intelligence
13. Evolutionary Computation
14. Swarm Intelligence
Part 4: Neural Intelligence
15. Neural Networks and Deep Learning
Part 5: Language Understanding
16. Natural Language Understanding
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
Item type: Books List(s) this item appears in: Computer Science & Engineering | New Arrival Book 2023
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Holdings
Item type Current library Collection Shelving location Call number Copy number Status Date due Barcode
Books Books KU Central Library Rack No. : 01 Annex : 01 Shelve No. : A-03 Reference Section (Non-Issuable Books) 006.3 NEA 2018 (Browse shelf(Opens below)) C-1 (NI) Not For Loan 52073


Includes bibliographical references and index.

1. Introduction to Artificial Intelligence

Part 1: Logical Intelligence

2. Propositional Logic

3. First-Order Logic

4. Certain Knowledge Representation

5. Learning Deterministic Models

Part 2: Probabilistic Intelligence

6. Probability

7. Uncertain Knowledge Representation

8. Advanced Properties of Bayesian Network

9. Decision Analysis

10. Learning Probabilistic Model Parameters

11. Learning Probabilistic Model Structure

12. Unsupervised Learning and Reinforcement Learning

Part 3: Emergent Intelligence

13. Evolutionary Computation

14. Swarm Intelligence

Part 4: Neural Intelligence

15. Neural Networks and Deep Learning

Part 5: Language Understanding

16. Natural Language Understanding

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

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