Custom cover image
Custom cover image

Mathematical Introduction to Data Science / by Sven A. Wegner

By: Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2024Publisher: Berlin, Heidelberg : Imprint: Springer, 2024Edition: 1st ed. 2024Description: 1 Online-Ressource(IX, 299 p. 119 illus.)ISBN:
  • 9783662694268
Subject(s): Genre/Form: Additional physical formats: 9783662694251 | 9783662694275 | Erscheint auch als: 9783662694251 Druck-Ausgabe | Erscheint auch als: 9783662694275 Druck-Ausgabe | Erscheint auch als: Mathematical introduction to data science. Druck-Ausgabe Berlin : Springer, 2024. ix, 299 SeitenDDC classification:
  • 001.422 23
  • 005.7 23
DOI: DOI: 10.1007/978-3-662-69426-8Online resources: Summary: 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.Summary: 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).PPN: PPN: 1900992515Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-SMA | ZDB-2-SXMS
No physical items for this record
KIT – The Research University in the Helmholtz Association                         Home | Impressum | Datenschutz | Barrierefreiheit | KIT