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{ "item_title" : "Machine Unlearning", "item_author" : [" Bitan Misra", "Sayan Chakraborty", "Nilanjan Dey "], "item_description" : "This book explores one of the most critical and emerging fields in artificial intelligence, machine unlearning. As data privacy concerns grow and regulations like GDPR demand compliance, this book provides a comprehensive guide to selectively removing learned information from machine learning models without sacrificing performance or requiring complete retraining. Covering foundational principles, advanced algorithms, benchmarking tools, and real-world case studies in healthcare, finance, and social media, the book bridges the gap between theory and practice. It also addresses ethical, legal, and societal implications, offering insights into creating trustworthy AI systems. This book is an essential resource for understanding and implementing machine unlearning in the era of responsible AI. It is for researchers, AI practitioners, and professionals seeking to understand and implement machine unlearning for data privacy, ethical AI design, and regulatory compliance. ", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/1/04/129/531/1041295316_b.jpg", "price_data" : { "retail_price" : "115.99", "online_price" : "115.99", "our_price" : "115.99", "club_price" : "115.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Machine Unlearning|Bitan Misra

Machine Unlearning : Principles, Methods, and Evolving Frontiers

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Overview

This book explores one of the most critical and emerging fields in artificial intelligence, machine unlearning. As data privacy concerns grow and regulations like GDPR demand compliance, this book provides a comprehensive guide to selectively removing learned information from machine learning models without sacrificing performance or requiring complete retraining. Covering foundational principles, advanced algorithms, benchmarking tools, and real-world case studies in healthcare, finance, and social media, the book bridges the gap between theory and practice. It also addresses ethical, legal, and societal implications, offering insights into creating trustworthy AI systems. This book is an essential resource for understanding and implementing machine unlearning in the era of responsible AI. It is for researchers, AI practitioners, and professionals seeking to understand and implement machine unlearning for data privacy, ethical AI design, and regulatory compliance.

This item is Non-Returnable

Details

  • ISBN-13: 9781041295310
  • ISBN-10: 1041295316
  • Publisher: CRC Press
  • Publish Date: September 2026
  • Page Count: 136

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