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Contemporary Artificial Intelligence, by Richard E. Neapolitan, Xia Jiang
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The notion of artificial intelligence (AI) often sparks thoughts of characters from science fiction, such as the Terminator and HAL 9000. While these two artificial entities do not exist, the algorithms of AI have been able to address many real issues, from performing medical diagnoses to navigating difficult terrain to monitoring possible failures of spacecrafts. Exploring these algorithms and applications, Contemporary Artificial Intelligence presents strong 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.
One of the first AI texts accessible to students, the book focuses on the most useful problem-solving strategies that have emerged from AI. In a student-friendly way, the authors cover logic-based methods; probability-based methods; emergent intelligence, including evolutionary computation and swarm intelligence; data-derived logical and probabilistic learning models; and natural language understanding. Through reading this book, students discover the importance of AI techniques in computer science.
- Sales Rank: #1301871 in Books
- Published on: 2012-08-25
- Original language: English
- Number of items: 1
- Dimensions: 9.50" h x 1.10" w x 6.10" l, 1.80 pounds
- Binding: Hardcover
- 515 pages
About the Author
Richard E. Neapolitan is a professor of computer science at Northeastern Illinois University. Dr. Neapolitan has published in prestigious journals related to reasoning under uncertainty and has authored five books: Probabilistic Reasoning in Expert Systems, Learning Bayesian Networks, Foundations of Algorithms, Probabilistic Methods for Financial and Marketing Informatics, and Probabilistic Methods for Bioinformatics. His research interests include probability and statistics, expert systems, cognitive science, and applications of probabilistic modeling to fields such as medicine, biology, and finance.
Xia Jiang is an assistant professor in the Department of Biomedical Informatics at the University of Pittsburgh. Dr. Jiang co-authored Probabilistic Methods for Financial and Marketing Informatics. Her current research focuses on developing novel algorithms/systems that improve the computational efficiency of large data analysis and network modeling of cancer genome data.
Most helpful customer reviews
1 of 1 people found the following review helpful.
A future classic: should be one of the the first A.I. textbook you read, a pedagogical yet rigorous introduction to the field
By A. T. Ko
I'm not sure why this book isn't more popular than it deserves to be. Everything is written in a concise, pedagogical style and easily the most approachable Introduction to A.I. book I have ever read. If you struggled with Russell/Norvig (Artificial Intelligence: A modern approach), which tend to be more theory based with little examples to solidify the explanation, this book takes the same approach but contain many examples to help you understand the theory. This book has a heavy emphasis on the probabilistic models used in A.I. (e.g.Bayesian learning), but it is by no means terse on other A.I techniques such as Logical A.I. and Machine learning . The set-up of the chapters and topics are similar to Russell/Norvig book (sans the searching methods that is only in Russell/Norvig), so it makes a great companion textbook for the corresponding chapters. However, this book has chapters on evolutionary computing and swarm intelligence that Russell/Norvig did not include as a topic, these chapters alone are worthy of the purchase of this book given that there are few books out there that give such a approachable and easily comprehensible introduction to these topics.
This book will be a future classic in the field and I look forward to future updates to this masterpiece of A.I. teaching
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