Artificial Intelligence and Machine Learning for Safety-Critical Systems: A Comprehensive Guide serves as a vital reference for engineers and system designers seeking to integrate AI and ML techniques into safety-critical environments. The book is meticulously structured into nine sections, each focusing on core applications and challenges unique to these high-stakes systems. Readers are guided through strategies that optimize resources, minimize failures, and bolster both system and public safety. With its practical approach, the guide aims to bridge the gap between advanced AI solutions and the rigorous demands of safety-critical industries.

The book also delves into diverse domains such as pattern recognition, image processing, edge computing, IoT, encryption, and hardware accelerators. Each application area is explored to reveal the unique hurdles and solutions in deploying ML models in safety-sensitive contexts. Finally, the authors also emphasize the importance of explainable AI, ensuring model outputs are transparent and trustworthy rather than opaque. To further strengthen confidence in these systems, the text discusses legal, certification, and regulatory aspects, equipping readers with the tools necessary to achieve compliance and public trust.

• Covers foundational concepts, advanced theories, and real-world applications, ensuring readers gain a thorough understanding of AI/ML as it applies to Safety-Critical Systems
• Presents both the risks and advantages of implementing machine learning techniques in Safety-Critical Systems
• Presents machine learning techniques in Safety-Critical Systems across domains, including pattern recognition, image processing, edge computing, Internet of Things (IoT), encryption, hardware accelerators, and many others
• Demonstrates how to meet legal certification and regulatory requirements for the appropriate ML models