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Explorations In Numerical Analysis And Machine Learning With Julia

Explorations In Numerical Analysis And Machine Learning With Julia

Explorations In Numerical Analysis And Machine Learning With Julia
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Explorations In Numerical Analysis And Machine Learning With Julia Paperback - 2025

by Lambers, James V (Author)/ Mooney, Amber C Sumner (Author)/ Montiforte, Vivian Ashley (Author)/ Quinlan, James (Author)

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World Scientific Publishing Co Pte Ltd, 2025. Paperback. New. 876 pages. 6.69x0.73x9.61 inches.
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Details

  • Title Explorations In Numerical Analysis And Machine Learning With Julia
  • Author Lambers, James V (Author)/ Mooney, Amber C Sumner (Author)/ Montiforte, Vivian Ashley (Author)/ Quinlan, James (Author)
  • Binding Paperback
  • Condition New
  • Pages 876
  • Volumes 1
  • Language ENG
  • Publisher World Scientific Publishing Co Pte Ltd
  • Publication date 2025
  • Bookseller's Inventory # x-9819819482
  • ISBN 9789819819485 / 9819819482
  • Weight 3.05 lbs (1.38 kg)
  • Dimensions 9.61 x 6.41 x 1.51 in (24.41 x 16.28 x 3.84 cm)
  • Category Mathematics
  • Quantity available 2

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From the publisher

The textbook is an expansion of Explorations in Numerical Analysis that includes new chapters covering topics from machine learning. It is intended for advanced undergraduate and early graduate students, with a focus on the connections between numerical analysis and machine learning.

Topics covered include computer arithmetic, error analysis, solution of systems of linear equations by direct and iterative methods, least squares problems, eigenvalue problems, nonlinear equations, optimization, polynomial interpolation and approximation, numerical differentiation and integration, ordinary differential equations, partial differential equations, machine learning, classification, regression, and neural networks.

Each problem is presented with derivations of solution techniques, analysis of their efficiency, accuracy and robustness, and detailed implementation using the Julia programming language. This book is suitable for a year-long course in numerical analysis, or for a one-semester course in numerical linear algebra (Part II) or machine learning (Part VI).

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