Machine Unlearning: Concepts, Techniques and Applications: DE (Studies in Computational Intelligence, 1270) - Hardcover

Gupta, Rajan; Gupta, Anamika; Kochhar, Sarabjeet Kaur; Pal, Saibal K.

Hardcover

ISBN 10: 3032184460 ISBN 13: 9783032184467

Verlag: Springer, 2026

Inhaltsangabe

This book masters the critical skill of selective data removal from AI systems—essential for regulatory compliance and ethical AI development. This comprehensive book bridges the gap between theoretical foundations and practical implementation, offering clear pathways through both exact and approximate unlearning methodologies. Designed for machine learning engineers, privacy specialists, researchers, and policymakers, it uniquely integrates technical depth with legal and ethical frameworks. From telecom to finance, discover how to eliminate data influence while preserving model utility. Ideal for graduate courses, professional training, and organizational compliance initiatives, this book positions you at the forefront of responsible AI innovation in an increasingly privacy-conscious world.

Über diesen Titel

Über die Autorin bzw. den Autor

Dr. Rajan Gupta is a Research and Analytics Professional, certified from INFORMS (CAP), ASA (GStat), UGC (NET), and CDC (CMC). He is currently serving as Assistant Professor at the University of Delhi, India, and is CAP Ambassador-Asia Region for INFORMS, USA. Dr. Saibal K. Pal is a Senior Scientist and former Chief Information Security Officer (CISO) at the Defence Research & Development Organization (DRDO), India.

Von der hinteren Coverseite

This book masters the critical skill of selective data removal from AI systems—essential for regulatory compliance and ethical AI development. This comprehensive book bridges the gap between theoretical foundations and practical implementation, offering clear pathways through both exact and approximate unlearning methodologies. Designed for machine learning engineers, privacy specialists, researchers, and policymakers, it uniquely integrates technical depth with legal and ethical frameworks. From telecom to finance, discover how to eliminate data influence while preserving model utility. Ideal for graduate courses, professional training, and organizational compliance initiatives, this book positions you at the forefront of responsible AI innovation in an increasingly privacy-conscious world.

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2026
• Sprache: Englisch
• ISBN 10: 3032184460
• ISBN 13: 9783032184467
• Einband: Gebundene Ausgabe
• Anzahl der Seiten: 203
• Kontakt zum Hersteller: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Deutschland