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

By: Material type: TextPublication details: Cambridge, United Kingdom : Cambridge University Press, 2012.Edition: 1st ed., reprintedDescription: xvii, 396 p. : charts, forms, tables ; 25 cmISBN:
  • 9781107096394 (hbk.)
  • 1107096391 (hbk.)
  • 9781107422223 (pbk.)
  • 1107422221 (pbk.)
Subject(s): Genre/Form: DDC classification:
  • 006.31 22 FLA
Contents:
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. Model ensembles-12. Machine learning experiments-Epilogue : where to go from here.
Abstract: 'Machine Learning' brings together all the state-of-the-art methods for making sense of data. With hundreds of worked examples and explanatory figures, it explains the principles behind these methods in an intuitive yet precise manner and will appeal to novice and experienced readers alike.
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Holdings
Cover image Item type Current library Home library Collection Shelving location Call number Materials specified Vol info URL Copy number Status Notes Date due Barcode Item holds Item hold queue priority Course reserves
Book - Borrowing Central Library Lower Floor Baccah 006.31 FLA (Browse shelf(Opens below)) 21291 Available 000037201
Total holds: 0

Reprint of the 2012 ed.

Index : p. 383-396.

Bibliography : p. 367-381.

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. Model ensembles-12. Machine learning experiments-Epilogue : where to go from here.

'Machine Learning' brings together all the state-of-the-art methods for making sense of data. With hundreds of worked examples and explanatory figures, it explains the principles behind these methods in an intuitive yet precise manner and will appeal to novice and experienced readers alike.

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