Mathematical Introduction to Data Science

Sven A. Wegner ()

in Springer Books from Springer

Date: 2024
ISBN: 978-3-662-69426-8
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Chapters in this book:

Ch Chapter 1 What Is Data (Science)?

Sven A. Wegner

Ch Chapter 10 Gaussian Random Vectors in High Dimensions

Sven A. Wegner

Ch Chapter 11 Dimensionality Reduction à la Johnson-Lindenstrauss

Sven A. Wegner

Ch Chapter 12 Separation and Fitting of High-Dimensional Gaussians

Sven A. Wegner

Ch Chapter 13 Perceptron

Sven A. Wegner

Ch Chapter 14 Support Vector Machines

Sven A. Wegner

Ch Chapter 15 Kernel Method

Sven A. Wegner

Ch Chapter 16 Neural Networks

Sven A. Wegner

Ch Chapter 17 Gradient Descent for Convex Functions

Sven A. Wegner

Ch Chapter 18 Selected Results of Probability Theory

Sven A. Wegner

Ch Chapter 2 Affine Linear, Polynomial and Logistic Regression

Sven A. Wegner

Ch Chapter 3 k-Nearest Neighbors

Sven A. Wegner

Ch Chapter 4 Clustering

Sven A. Wegner

Ch Chapter 5 Graph Clustering

Sven A. Wegner

Ch Chapter 6 Best-Fit Subspaces

Sven A. Wegner

Ch Chapter 7 Singular Value Decomposition

Sven A. Wegner

Ch Chapter 8 Curse and Blessing of High Dimensionality

Sven A. Wegner

Ch Chapter 9 Concentration of Measure

Sven A. Wegner

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DOI: 10.1007/978-3-662-69426-8

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