90,94 €
Versandkostenfrei per Post / DHL
Aktuell nicht verfügbar
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.
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
Springer-Verlag GmbH |
| 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 |