Springer Analytics Modeling in Reliability and Machine Learning - Springer

This comprehensive book explores cutting-edge research and practical applications across reliability engineering, statistics, and machine learning. Written by leading experts and published by Springer, it bridges analytical models with real-world solutions in data science, manufacturing, healthcare, and industrial settings. The work integrates machine learning algorithms, artificial intelligence, statistical inference, and optimization techniques to address modern challenges in the big data era. Designed for professionals and researchers seeking to understand how billions of connected devices generate and analyse data at scale, this volume combines theoretical foundations with actionable insights for applying advanced computational methods to solve complex reliability and machine learning problems in contemporary industries.