the art and science of algorithms that make sense of data Flach, Peter A. creator text Cambridge; |a New York Cambridge University Press 2012 monographic eng

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xvii, 396 pages : illustrations ; 25 cm. '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. 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. Peter Flach. Includes bibliographical references (p. 367-381) and index. Machine learning Textbooks Apprentissage automatique Manual scholary 9781107422223 9781107096394 PUCPR 210114 20210114194033.0