Charu C. Aggarwal: Artificial Intelligence

Cover: 9783030723569 | Artificial Intelligence | A Textbook | Charu C. Aggarwal | Buch | xx

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Beschreibung

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.

Über den Autor

Charu C. Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T. J. Watson Research Center in Yorktown Heights, New York. He completed his undergraduate degree in Computer Science from the Indian Institute of Technology at Kanpur in 1993 and his Ph.D. from the Massachusetts Institute of Technology in 1996. He has worked extensively in the field of data mining. He has published more than 400 papers in refereed conferences and journals and authored over 80 patents. He is the author or editor of 19 books, including textbooks on data mining, recommender systems, and outlier analysis. Because of the commercial value of his patents, he has thrice been designated a Master Inventor at IBM. He is a recipient of an IBM Corporate Award (2003) for his work on bio-terrorist threat detection in data streams, a recipient of the IBM Outstanding Innovation Award (2008) for his scientific contributions to privacy technology, and a recipient of two IBM Outstanding Technical Achievement Awards (2009, 2015) for his work on data streams/high-dimensional data. He received the EDBT 2014 Test of Time Award for his work on condensation-based privacy-preserving data mining. He is also a recipient of the IEEE ICDM Research Contributions Award (2015) and the ACM SIGKDD Innovations Award (2019), which are the two highest awards for influential research contributions in data mining.

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."

Zusammenfassung

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

Inhaltsverzeichnis

1 An Introduction to Artificial Intelligence.- 2 Searching State Spaces.- 3 Multiagent Search.- 4 Propositional Logic.- 5 First-Order Logic.- 6 Machine Learning: The Inductive View.- 7 Neural Networks.- 8 Domain-Specific Neural Architectures.- 9 Unsupervised Learning.- 10 Reinforcement Learning.- 11 Probabilistic Graphical Models.- 12 Knowledge Graphs.- 13 Integrating Reasoning and Learning.

Details

Erscheinungsjahr:	2021
Genre:	Informatik
Rubrik:	Naturwissenschaften & Technik
Medium:	Buch
Inhalt:	xx 483 S. 158 s/w Illustr. 15 farbige Illustr. 483 p. 173 illus. 15 illus. in color.
ISBN-13:	9783030723569
ISBN-10:	3030723569
Sprache:	Englisch
Ausstattung / Beilage:	HC runder Rücken kaschiert
Einband:	Gebunden
Autor:	Aggarwal, Charu C.
Auflage:	1st ed. 2021
Hersteller:	Springer International Publishing
Maße:	260 x 183 x 33 mm
Von/Mit:	Charu C. Aggarwal
Erscheinungsdatum:	17.07.2021
Gewicht:	1,143 kg

Artikel-ID: 119670379