Authors:
- Teaches artificial intelligence from a broad point of view, while including and integrating multiple schools of thought such as deductive reasoning and inductive learning
- Provides more balanced coverage, and also discusses how different schools of thought are related to one another, while most artificial intelligence books tend to focus on reasoning methods, treating machine learning in a limited way
- Newer technologies such as reinforcement learning and knowledge graphs are covered in detail
- Includes examples and exercises throughout the book
- Offers a solution manual for teaching instructors only
- Request lecturer material: sn.pub/lecturer-material
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Table of contents (14 chapters)
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Front Matter
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Back Matter
About this book
This textbook covers the broader field of artificial intelligence. The chapters for this textbook span within three categories:
- Deductive reasoning methods: These methods start with pre-defined hypotheses and reason with them in order to arrive at logically sound conclusions. The underlying methods include search and logic-based methods. These methods are discussed in Chapters 1through 5.
- Inductive Learning Methods: These methods start with examples and use statistical methods in order to arrive at hypotheses. Examples include regression modeling, support vector machines, neural networks, reinforcement learning, unsupervised learning, and probabilistic graphical models. These methods are discussed in Chapters~6 through 11.
- Integrating Reasoning and Learning: Chapters~11 and 12 discuss techniques for integrating reasoning and learning. Examples include the use of knowledge graphs and neuro-symbolic artificial intelligence.
The primary audience for this textbook are professors and advanced-level students in computer science. It is also possible to use this textbook for the mathematics requirements for an undergraduate data science course. Professionals working in this related field many also find this textbook useful as a reference.
Reviews
“The author has thoroughly researched all areas of AI in order to write this high-quality book. … This highly valuable book provides a vast overview of AI in a well-structured manner. It could be used as a textbook in graduate-level courses.” (J. Arul, Computing Reviews, December 12, 2022)
“This is very useful book for graduate students and researchers.” (T. C. Mohan, zbMATH 1477.68001, 2022)
Authors and Affiliations
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IBM T. J. Watson Research Center, Yorktown Heights, USA
Charu C. Aggarwal
About the author
He has served as the general co-chair of the IEEE Big Data Conference (2014) and as the program co-chair of the ACM CIKM Conference (2015), the IEEE ICDM Conference (2015), and the ACM KDD Conference (2016). He served as an associate editor of the IEEE Transactions on Knowledge and Data Engineering from 2004 to 2008. He is an associate editor of the IEEE Transactions on Big Data, an action editor of the Data Mining and Knowledge Discovery Journal, and an associate editor of the Knowledge and Information Systems Journal. He serves as the editor-in-chief of the ACM Transactions on Knowledge Discovery from Data as well as the ACM SIGKDD Explorations. He serves on the advisory board of the Lecture Notes on Social Networks, a publication by Springer. He has served as the vice-president of the SIAM Activity Group on Data Mining and is a member of the SIAM industry committee. He is a fellow of the SIAM, ACM, and the IEEE, for “contributions to knowledge discovery and data mining algorithms.”
Bibliographic Information
Book Title: Artificial Intelligence
Book Subtitle: A Textbook
Authors: Charu C. Aggarwal
DOI: https://doi.org/10.1007/978-3-030-72357-6
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-72356-9Published: 17 July 2021
Softcover ISBN: 978-3-030-72359-0Published: 18 July 2022
eBook ISBN: 978-3-030-72357-6Published: 16 July 2021
Edition Number: 1
Number of Pages: XX, 483
Number of Illustrations: 158 b/w illustrations, 15 illustrations in colour
Topics: Artificial Intelligence, Machine Learning, Data Mining and Knowledge Discovery