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Machine Unlearning
Paperback
Publisher: Independently Published
Published: Oct 1 2025
Pages: 172
Weight: 0.52
Height: 0.37 Width: 6.00 Depth: 9.00
Language: English
3. Addressing Regulatory and Ethical Concerns: Discussing compliance with GDPR, CCPA, and other data protection laws, along with ethical implications of AI memory retention.
4. Exploring Future Directions: Analyzing open research problems, scalability challenges, and the intersection of Unlearning with federated learning, reinforcement learning, and large language models (LLMs). Who Should Read This Book? This book is intended for: 1. AI/ML Engineers & Developers who need to implement compliant, adaptable AI systems.
2. Data Scientists & Researchers exploring privacy-preserving ML and regulatory constraints.
3. Cybersecurity & Privacy Experts working on data governance and AI auditing.
4. Policy Makers & Legal Professionals seeking technical insights into AI regulation. How to Use This Book: The book is structured into three main parts:
1. Foundations of Machine Unlearning (Chapters 1-3): Covers core concepts, threat models, and legal frameworks. 2. Algorithms & Implementation (Chapters 4-7): Details exact and approximate Unlearning methods with practical examples.
3. Advanced Topics & Future Directions (Chapters 8-10): Explores federated Unlearning, reinforcement learning, and open challenges.
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