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Mathematical Introduction to Data Science

Om Mathematical Introduction to Data Science

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.

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  • Språk:
  • Engelska
  • ISBN:
  • 9783662694251
  • Format:
  • Häftad
  • Sidor:
  • 299
  • Utgiven:
  • 31. augusti 2024
  • Utgåva:
  • 2024
  • Mått:
  • 155x235x0 mm.
  Fri leverans
Leveranstid: 2-4 veckor
Förväntad leverans: 27. december 2024
Förlängd ångerrätt till 31. januari 2025

Beskrivning av Mathematical Introduction to Data Science

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.

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