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

By: Flach, Peter A.
Material type: materialTypeLabelBookPublisher: Cambridge, United Kingdom : Cambridge University Press, 2012Edition: 1st ed., reprinted.Description: xvii, 396 p. : charts, forms, tables ; 25 cm.ISBN: 9781107096394 (hbk.); 1107096391 (hbk.); 9781107422223 (pbk.); 1107422221 (pbk.).Subject(s): Machine learning -- Textbooks | Engineering, Electrical Informatics and Computer Science December2014Genre/Form: -- Reading bookDDC classification: 006.31
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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Item type Current location Collection Call number Vol info Status Date due Barcode Item holds
Book - Borrowing Book - Borrowing Central Library
Lower Floor
Baccah 006.31 FLA (Browse shelf) 21291 Available 000037201
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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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Machine learning : by Flach, Peter A. ©2012

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