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Mathematical introduction to data science

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Mathematical introduction to data science/ by Sven A. Wegner.
作者:	Wegner, Sven A.
出版者:	Berlin, Heidelberg :Springer Berlin Heidelberg : : 2024.,
面頁冊數:	ix, 299 p. :ill., digital ;24 cm.
內容註:	Preface -- 1 What is Data (Science)? -- 2 Affine Linear, Polynomial and Logistic Regression -- 3 k-nearest Neighbors -- 4 Clustering -- 5 Graph Clustering -- 6 Best-Fit Subspaces -- 7 Singular Value Decomposition -- 8 Curse and Blessing of High Dimensionality -- 9 Concentration of Measure -- 10 Gaussian Random Vectors in High Dimensions -- 11 Dimensionality Reduction à la Johnson-Lindenstrauss -- 12 Separation and Fitting of HIgh-Dimensional Gaussians -- 13 Perceptron -- 14 Support Vector Machines -- 15 Kernel Method -- 16 Neural Networks -- 17 Gradient Descent for Convex Functions -- Appendix: Selected Results of Probability Theory -- Bibliography -- Index.
Contained By:	Springer Nature eBook
標題:	Mathematical analysis. -
電子資源:	https://doi.org/10.1007/978-3-662-69426-8
ISBN:	9783662694268

Mathematical introduction to data science
Wegner, Sven A.

Mathematical introduction to data science[electronic resource] /by Sven A. Wegner. - Berlin, Heidelberg :Springer Berlin Heidelberg :2024. - ix, 299 p. :ill., digital ;24 cm.

Preface -- 1 What is Data (Science)? -- 2 Affine Linear, Polynomial and Logistic Regression -- 3 k-nearest Neighbors -- 4 Clustering -- 5 Graph Clustering -- 6 Best-Fit Subspaces -- 7 Singular Value Decomposition -- 8 Curse and Blessing of High Dimensionality -- 9 Concentration of Measure -- 10 Gaussian Random Vectors in High Dimensions -- 11 Dimensionality Reduction à la Johnson-Lindenstrauss -- 12 Separation and Fitting of HIgh-Dimensional Gaussians -- 13 Perceptron -- 14 Support Vector Machines -- 15 Kernel Method -- 16 Neural Networks -- 17 Gradient Descent for Convex Functions -- Appendix: Selected Results of Probability Theory -- Bibliography -- Index.

This textbook is intended for students of mathematics who have completed the foundational courses of their undergraduate studies and now want to specialize in Data Science and Machine Learning. It introduces the reader to the most important topics in the latter areas focusing on rigorous proofs and a systematic understanding of the underlying ideas. The textbook comes with 121 classroom-tested exercises. Topics covered include k-nearest neighbors, linear and logistic regression, clustering, best-fit subspaces, principal component analysis, dimensionality reduction, collaborative filtering, perceptron, support vector machines, the kernel method, gradient descent and neural networks. The author Sven A. Wegner earned his PhD in Functional Analysis in 2010. After several international academic positions, he is currently affiliated with the University of Hamburg (Germany)

ISBN: 9783662694268

Standard No.: 10.1007/978-3-662-69426-8doiSubjects--Topical Terms:

516833
Mathematical analysis.

LC Class. No.: QA300

Dewey Class. No.: 515

Mathematical introduction to data science
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505 0 $a Preface -- 1 What is Data (Science)? -- 2 Affine Linear, Polynomial and Logistic Regression -- 3 k-nearest Neighbors -- 4 Clustering -- 5 Graph Clustering -- 6 Best-Fit Subspaces -- 7 Singular Value Decomposition -- 8 Curse and Blessing of High Dimensionality -- 9 Concentration of Measure -- 10 Gaussian Random Vectors in High Dimensions -- 11 Dimensionality Reduction à la Johnson-Lindenstrauss -- 12 Separation and Fitting of HIgh-Dimensional Gaussians -- 13 Perceptron -- 14 Support Vector Machines -- 15 Kernel Method -- 16 Neural Networks -- 17 Gradient Descent for Convex Functions -- Appendix: Selected Results of Probability Theory -- Bibliography -- Index.
520 $a This textbook is intended for students of mathematics who have completed the foundational courses of their undergraduate studies and now want to specialize in Data Science and Machine Learning. It introduces the reader to the most important topics in the latter areas focusing on rigorous proofs and a systematic understanding of the underlying ideas. The textbook comes with 121 classroom-tested exercises. Topics covered include k-nearest neighbors, linear and logistic regression, clustering, best-fit subspaces, principal component analysis, dimensionality reduction, collaborative filtering, perceptron, support vector machines, the kernel method, gradient descent and neural networks. The author Sven A. Wegner earned his PhD in Functional Analysis in 2010. After several international academic positions, he is currently affiliated with the University of Hamburg (Germany)
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