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A Concise Introduction To Machine Learning - Faul

A Concise Introduction To Machine Learning - Faul

A Concise Introduction To Machine Learning - Faul
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A Concise Introduction To Machine Learning - Faul Papeback - 2019

by A. C. Faul

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Taylor & Francis Group . Papeback. New.
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Details

  • Title A Concise Introduction To Machine Learning - Faul
  • Author A. C. Faul
  • Binding Papeback
  • Condition New
  • Pages 334
  • Volumes 1
  • Language ENG
  • Publisher Taylor & Francis Group
  • Publication date 2019-08-12
  • Illustrated Yes
  • Bookseller's Inventory # 6376428486
  • ISBN 9780815384106 / 0815384106
  • Weight 1 lbs (0.45 kg)
  • Dimensions 9.1 x 6.1 x 0.7 in (23.11 x 15.49 x 1.78 cm)
  • Category Business / Economics / Finance
  • Library of Congress subjects Machine learning
  • Library of Congress Catalogue Number 2019015915
  • Dewey Decimal Code 006.31
  • Quantity available 4

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From the publisher

The emphasis of the book is on the question of Why - only if why an algorithm is successful is understood, can it be properly applied, and the results trusted. Algorithms are often taught side by side without showing the similarities and differences between them. This book addresses the commonalities, and aims to give a thorough and in-depth treatment and develop intuition, while remaining concise.

This useful reference should be an essential on the bookshelves of anyone employing machine learning techniques.

The author's webpage for the book can be accessed here.

About the author

A.C. Faul was a Teaching Associate, Fellow and Director of Studies in Mathematics at Selwyn College, University of Cambridge. She came to Cambridge after studying two years in Germany. She did Part II and Part III Mathematics at Churchill College, Cambridge. Since these are only two years, and three years are necessary for a first degree, she does not hold one. However, this was followed by a PhD on the Faul-Powell Algorithm for Radial Basis Function Interpolation under the supervision of Professor Mike Powell. She then worked on the Relevance Vector Machine with Mike Tipping at Microsoft Research Cambridge. Ten years in industry followed where she worked on various algorithms on mobile phone networks, image processing and data visualization. Current projects are on machine learning techniques. In teaching, she enjoys to bring out the underlying, connecting principles of algorithms, which is the emphasis of a book on Numerical Analysis she has written.

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