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Mathematical Introduction to Data Science

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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...

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Furkejuvvon:
Bibliográfalaš dieđut
Váldodahkki: Wegner, Sven A. (Dahkki)
Searvvušdahkki: SpringerLink (Online service)
Materiálatiipa: Elektrovnnalaš E-girji
Giella:eaŋgalasgiella
Almmustuhtton: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2024.
Preanttus:1st ed. 2024.
Fáttát:
Quantitative research.
Artificial intelligence > Data processing.
Data Analysis and Big Data.
Data Science.
Liŋkkat:https://doi.org/10.1007/978-3-662-69426-8
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Sisdoallologahallan:
  • 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.

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