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Machine learning : the art and science of algorithms that make sense of data / Peter Flach

By: Material type: TextTextPublication details: Cambridge : Cambridge University Press, 2012Description: xvii, 396 p. : illISBN:
  • 9781107422223
  • 1107422221
Subject(s): DDC classification:
  • 006.31 FLA
Contents:
Prologue: a machine learning sampler; 1. The ingredients of machine learning; Binary classification and related tasks; 3. Beyond binary classification; 4. Concept learning; 5. Tree models; 6. Rule models; 7. Linear models; 8. Distance-based models; 9. Probabilistic models; 10. Features; 11. In brief: model ensembles; 12. In brief: machine learning experiments; Epilogue: where t go from here; Important points to remember; Bibliography; Index
Holdings
Item type Current library Call number Copy number Status Date due Barcode
Standard Loan Moylish Library Main Collection 006.31 FLA (Browse shelf(Opens below)) 1 Available 39002100620757

Enhanced descriptions from Syndetics:

As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.

Prologue: a machine learning sampler; 1. The ingredients of machine learning; Binary classification and related tasks; 3. Beyond binary classification; 4. Concept learning; 5. Tree models; 6. Rule models; 7. Linear models; 8. Distance-based models; 9. Probabilistic models; 10. Features; 11. In brief: model ensembles; 12. In brief: machine learning experiments; Epilogue: where t go from here; Important points to remember; Bibliography; Index

Table of contents provided by Syndetics

  • Prologue: a machine learning sampler
  • 1 The ingredients of machine learning
  • 2 Binary classification and related tasks
  • 3 Beyond binary classification
  • 4 Concept learning
  • 5 Tree models
  • 6 Rule models
  • 7 Linear models
  • 8 Distance-based models
  • 9 Probabilistic models
  • 10 Features
  • 11 In brief: model ensembles
  • 12 In brief: machine learning experiments
  • Epilogue: where to go from here
  • Important points to remember
  • Bibliography
  • Index

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