Mathematics and statistics provide the foundational principles for understanding and building machine learning models. These disciplines offer the tools to analyze data, quantify uncertainty, and optimize model performance. Key areas include linear algebra for data representation and manipulation, calculus for optimization algorithms, probability theory for handling uncertainty, and statistical inference for concluding data.
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