Items related to Adversarial Learning and Secure AI
Synopsis
The first textbook on adversarial machine learning, including both attacks and defenses, background material, and hands-on student projects.
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About the Authors
David J. Miller is Professor of Electrical Engineering at the Pennsylvania State University.
Zhen Xiang is a post-doctoral research associate in Computer Science at the University of Illinois, Urbana-Champaign.
George Kesidis is Professor of Computer Science and Engineering, and of Electrical Engineering, at the Pennsylvania State University.
"About this title" may belong to another edition of this title.
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Adversarial Learning and Secure AI
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Cambridge University Press, 2023
ISBN 10: 1009315676
ISBN 13: 9781009315678
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Adversarial Learning and Secure AI
Published by
Cambridge University Press, 2023
ISBN 10: 1009315676
ISBN 13: 9781009315678
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Adversarial Learning and Secure AI
Published by
Cambridge University Press, 2023
ISBN 10: 1009315676
ISBN 13: 9781009315678
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Adversarial Learning And Secure Ai
Published by
Cambridge University Press, 2023
ISBN 10: 1009315676
ISBN 13: 9781009315678
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Adversarial Learning and Secure AI
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Cambridge University Press, 2023
ISBN 10: 1009315676
ISBN 13: 9781009315678
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Adversarial Learning And Secure Ai
Published by
Cambridge University Press, 2023
ISBN 10: 1009315676
ISBN 13: 9781009315678
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Adversarial Learning and Secure AI (Hardcover)
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Cambridge University Press, Cambridge, 2023
ISBN 10: 1009315676
ISBN 13: 9781009315678
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Hardcover. Condition: new. Hardcover. Providing a logical framework for student learning, this is the first textbook on adversarial learning. It introduces vulnerabilities of deep learning, then demonstrates methods for defending against attacks and making AI generally more robust. To help students connect theory with practice, it explains and evaluates attack-and-defense scenarios alongside real-world examples. Feasible, hands-on student projects, which increase in difficulty throughout the book, give students practical experience and help to improve their Python and PyTorch skills. Book chapters conclude with questions that can be used for classroom discussions. In addition to deep neural networks, students will also learn about logistic regression, naive Bayes classifiers, and support vector machines. Written for senior undergraduate and first-year graduate courses, the book offers a window into research methods and current challenges. Online resources include lecture slides and image files for instructors, and software for early course projects for students. Designed for upper undergraduate and graduate courses on adversarial learning and AI security, this textbook connects theory with practice using real-world examples, case studies, and hands-on student projects. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781009315678
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Adversarial Learning and Secure AI
Published by
Cambridge University Press, 2023
ISBN 10: 1009315676
ISBN 13: 9781009315678
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Adversarial Learning And Secure Ai
Published by
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ISBN 10: 1009315676
ISBN 13: 9781009315678
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Adversarial Learning And Secure Ai
Published by
Cambridge University Press, 2023
ISBN 10: 1009315676
ISBN 13: 9781009315678
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