| 000 | 02275nam a22002057a 4500 | ||
|---|---|---|---|
| 999 |
_c2485 _d2485 |
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| 003 | OSt | ||
| 005 | 20200224120458.0 | ||
| 008 | 200118b ||||| |||| 00| 0 eng d | ||
| 020 | _a978-1-316-50611-0 | ||
| 028 |
_bAllied Informatics, Jaipur _c7084 _d13/01/2020 _q2019-20 |
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| 040 |
_aBSDU _bEnglish _cBSDU |
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| 082 |
_a006.31 _bFLA |
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| 100 | _aFlach,Peter | ||
| 245 | _aMachine Learning:The art and science of algorithm that make sense of Data | ||
| 260 |
_aNew Delhi _bCambridge University Press _c2019 |
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| 300 | _a396 | ||
| 504 | _a 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 and Chapter 1 are freely available online Pedagogic features include boxes summarising relevant background material and a list of 'important points to remember' Epilogue includes open problems in machine learning Contents 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. | ||
| 650 | _aMachine Learning | ||
| 942 |
_2ddc _cBK |
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