Artificial Intelligence: A Textbook
Autor Charu C. Aggarwalen Limba Engleză Paperback – 18 iul 2022
- 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.
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Specificații
ISBN-13: 9783030723590
ISBN-10: 3030723593
Pagini: 483
Ilustrații: XX, 483 p. 173 illus., 15 illus. in color.
Dimensiuni: 178 x 254 x 31 mm
Greutate: 0.87 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
ISBN-10: 3030723593
Pagini: 483
Ilustrații: XX, 483 p. 173 illus., 15 illus. in color.
Dimensiuni: 178 x 254 x 31 mm
Greutate: 0.87 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
Cuprins
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.
Recenzii
“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)Notă biografică
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.”
Textul de pe ultima copertă
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.
Caracteristici
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