Adversarial Machine Learning - Rilegato

Joseph, Anthony D.; Nelson, Blaine; Rubinstein, Benjamin I. P.; Tygar, J. D.

Rilegato

ISBN 10: 1107043468 ISBN 13: 9781107043466

Casa editrice: Cambridge University Press, 2019

Riassunto

This study allows readers to get to grips with the conceptual tools and practical techniques for building robust machine learning in the face of adversaries.

Su questo libro

Informazioni sugli autori

Anthony D. Joseph is a Chancellor's Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He was formerly the Director of Intel Labs Berkeley.

Blaine Nelson is a Software Engineer in the Software Engineer in the Counter-Abuse Technologies (CAT) team at Google. He has previously worked at the University of Potsdam and the University of Tübingen.

Benjamin I. P. Rubinstein is a Senior Lecturer in Computing and Information Systems at the University of Melbourne. He has previously worked at Microsoft Research, Google Research, Yahoo! Research, Intel Labs Berkeley, and IBM Research.

J. D. Tygar is a Professor of Computer Science and a Professor of Information Management at the University of California, Berkeley.

Dettagli dell'edizione

• Editore: Cambridge University Press
• Data di pubblicazione: 2019
• Lingua: Inglese
• ISBN 10: 1107043468
• ISBN 13: 9781107043466
• Rilegatura: Copertina rigida
• Numero di pagine: 338
• Contatto del produttore: non disponibile
• Persona responsabile: Avery; https://www.avery.fr/; ; chemin des peupliers 27; Dardilly; Francia