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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 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.
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).
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.
| Erscheinungsjahr: | 2024 |
|---|---|
| Genre: | Informatik, Mathematik, Medizin, Naturwissenschaften, Technik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: |
ix
299 S. 119 s/w Illustr. 299 p. 119 illus. |
| ISBN-13: | 9783662694251 |
| ISBN-10: | 3662694255 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | Wegner, Sven A. |
| Hersteller: |
Springer-Verlag GmbH
Springer Berlin Heidelberg |
| Verantwortliche Person für die EU: | Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com |
| Maße: | 235 x 155 x 16 mm |
| Von/Mit: | Sven A. Wegner |
| Erscheinungsdatum: | 31.08.2024 |
| Gewicht: | 0,532 kg |
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).
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.
| Erscheinungsjahr: | 2024 |
|---|---|
| Genre: | Informatik, Mathematik, Medizin, Naturwissenschaften, Technik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: |
ix
299 S. 119 s/w Illustr. 299 p. 119 illus. |
| ISBN-13: | 9783662694251 |
| ISBN-10: | 3662694255 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | Wegner, Sven A. |
| Hersteller: |
Springer-Verlag GmbH
Springer Berlin Heidelberg |
| Verantwortliche Person für die EU: | Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com |
| Maße: | 235 x 155 x 16 mm |
| Von/Mit: | Sven A. Wegner |
| Erscheinungsdatum: | 31.08.2024 |
| Gewicht: | 0,532 kg |
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