{"product_id":"artificial-intelligence-a-textbook-hardcover","title":"Artificial Intelligence: A Textbook - Hardcover","description":"\u003cp\u003eby \u003cb\u003eCharu C. Aggarwal\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis textbook covers the broader field of artificial intelligence. The chapters for this textbook span within three categories: \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e\u003cli\u003e\n\u003cb\u003eDeductive reasoning methods: \u003c\/b\u003e 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.\u003c\/li\u003e\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e\u003cli\u003e\n\u003cb\u003eInductive Learning Methods: \u003c\/b\u003e 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. \u003c\/li\u003e\u003c\/ul\u003e\u003cul\u003e\u003cli\u003e\n\u003cb\u003eIntegrating Reasoning and Learning: \u003c\/b\u003e Chapters 11 and 12 discuss techniques for integrating reasoning and learning. Examples include the use of knowledge graphs and neuro-symbolic artificial intelligence.\u003c\/li\u003e\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThe 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.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis textbook covers the broader field of artificial intelligence. The chapters for this textbook span within three categories: \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e\u003cli\u003e\n\u003cb\u003eDeductive reasoning methods: \u003c\/b\u003e 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.\u003c\/li\u003e\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e\u003cli\u003e\n\u003cb\u003eInductive Learning Methods: \u003c\/b\u003e 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. \u003c\/li\u003e\u003c\/ul\u003e\u003cul\u003e\u003cli\u003e\n\u003cb\u003eIntegrating Reasoning and Learning: \u003c\/b\u003e Chapters 11 and 12 discuss techniques for integrating reasoning and learning. Examples include the use of knowledge graphs and neuro-symbolic artificial intelligence.\u003c\/li\u003e\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThe 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.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003cb\u003eCharu C. Aggarwal\u003c\/b\u003e 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.\u003cbr\u003eHe 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.\"\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 483\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.06 x 10 x 7 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e July 17, 2021\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":42100182745223,"sku":"9783030723569","price":105.28,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0601\/2623\/2711\/files\/6ecc05a8d0163e8146e02095ca39d527.webp?v=1732382011","url":"https:\/\/booksby.splitshops.com\/products\/artificial-intelligence-a-textbook-hardcover","provider":"Books by splitShops","version":"1.0","type":"link"}