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This document provides a tutorial on uncertainty quantification (UQ) in machine learning (ML) for engineering design and health prognostics, emphasizing its importance for safety assurance and decision-making. It covers various UQ methods, including Gaussian process regression and Bayesian neural networks, and discusses their applications in predicting the remaining useful life of lithium-ion batteries and turbofan engines. The tutorial aims to enhance understanding of predictive uncertainty and its implica

Author

Amirhossein Shokrani

Language

EN