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Statistical Learning, as a part of Mathematics within the Science/Statistics hierarchy, focuses on developing and applying statistical models to learn patterns and make predictions from data. It encompasses various techniques such as regression analysis, classification algorithms, and dimensionality reduction methods. The goal is to understand and extract useful information from complex datasets, often with the aim of informing decision-making or guiding further research in fields like machine learning, artificial intelligence, and data science. By leveraging statistical principles and computational tools, Statistical Learning enables researchers and practitioners to draw meaningful insights from data while accounting for uncertainty and noise.