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Understanding Uncertainty in Machine Learning
This chapter explores the theme of uncertainty quantification (UQ) in machine learning, emphasizing its role in improving decision-making through reliable predictions. It differentiates between epistemic and aleatoric uncertainties, discussing methods to estimate them and their significance in scenarios like active learning and outlier detection. The chapter also highlights the contributions of key research and practical techniques such as dropout and snapshot ensembles in addressing these complexities.