Mathematics of Data Science: A Rigorous Foundation for Data Science Algorithms and Techniques

This book provides a rigorous mathematical foundation for data science, covering topics like high-dimensional data analysis, dimensionality reduction techniques, optimization, and deep learning. It emphasizes theoretical frameworks for understanding core data science methods including singular value decomposition, graph laplacians, and matrix concentration inequalities.

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