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Model-Based Machine Learning

Model-Based Machine Learning

Model-Based Machine Learning
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Model-Based Machine Learning Hardback - 2023

by Winn, John

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Crc Press, 2023-10-26. 1. hardcover. New. 5.75x0.75x9.00. Buy with confidence. Excellent Customer Service & Return policy.
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Details

  • Title Model-Based Machine Learning
  • Author Winn, John
  • Binding Hardback
  • Edition 1
  • Condition New
  • Pages 455
  • Volumes 1
  • Language ENG
  • Publisher Crc Press
  • Publication date 2023-10-26
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index
  • Bookseller's Inventory # DADAX1498756816
  • ISBN 9781498756815 / 1498756816
  • Weight 2.1 lbs (0.95 kg)
  • Dimensions 9.3 x 6.3 x 1 in (23.62 x 16.00 x 2.54 cm)
  • Size 5.75x0.75x9.00
  • Category Business / Economics / Finance
  • Library of Congress subjects Machine learning, Machine learning - Mathematical models
  • Library of Congress Catalogue Number 2023015225
  • Dewey Decimal Code 006.31
  • Quantity available 1

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From the publisher

Today, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real world problem. This book tackles this challenge through model-based machine learning which focuses on understanding the assumptions encoded in a machine learning system and their corresponding impact on the behaviour of the system.

The key ideas of model-based machine learning are introduced through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter introduces one case study and works through step-by-step to solve it using a model-based approach. The aim is not just to explain machine learning methods, but also showcase how to create, debug, and evolve them to solve a problem.

Features:

  • Explores the assumptions being made by machine learning systems and the effect these assumptions have when the system is applied to concrete problems.
  • Explains machine learning concepts as they arise in real-world case studies.
  • Shows how to diagnose, understand and address problems with machine learning systems.
  • Full source code available, allowing models and results to be reproduced and explored.
  • Includes optional deep-dive sections with more mathematical details on inference algorithms for the interested reader.

About the author

John Winn is a Principal Researcher at Microsoft Research, UK.

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