[Submitted on 11 Jul 2026]
[View PDF](/pdf/2607.11938)
Abstract:This book is about the mathematical foundations of data science.
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Introduction
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Curses, Blessings, and Surprises in High Dimensions
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Singular Value Decomposition and Principal Component Analysis
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Linear Regression and Regularization
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Graphs, Networks, and Clustering
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Nonlinear Dimension Reduction and Diffusion Maps
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Linear Dimension Reduction via Random Projections
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Optimization for Data Science
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Classification
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A Mathematical Introduction to Deep Learning
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Large Sample Limit of Graph Laplacians
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Community
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Concentration of Measure and Gaussian Analysis
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Matrix Concentration Inequalities
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Compressive Sensing and Sparsity
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Low-Rank Matrix Recovery
Submission history #
From: Thomas Strohmer [[view email](/show-email/c0ae7ce9/2607.11938)]
**[v1]** Sat, 11 Jul 2026 08:31:44 UTC (15,747 KB)
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