Mathematics of Data Science

Short summary

This is a textbook covering mathematical foundations of data science across 16 chapters. Topics range from high-dimensional geometry, SVD/PCA, and linear regression to graph clustering, diffusion maps, random projections, optimization, classification, deep learning, and compressive sensing. It serves as a comprehensive reference for the mathematical underpinnings of modern data science methods.

• •Textbook covering 16 chapters of data science mathematical foundations
• •Topics include SVD/PCA, optimization, deep learning, compressive sensing, and concentration inequalities
• •Comprehensive reference for practitioners needing mathematical rigor behind data science methods

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