000			01512nam a22002537a 4500
003			PUCPR
005			20210114194033.0
008			210114b ||||| |||| 00| 0 eng d
020			_a9781107422223
020			_a9781107096394
040			_aPUCPR
049			_aPUCPR Arecibo
092			_2First _a006.31 _bF571m
100			_9297889 _aFlach, Peter A.
245			_aMachine Learning : _bthe art and science of algorithms that make sense of data / _cPeter Flach.
260			_aCambridge; |a New York : _bCambridge University Press, _c2012.
300			_axvii, 396 pages : _billustrations ; _c25 cm.
504			_aIncludes bibliographical references (p. 367-381) and index.
505			_a1. 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.
520			_a'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.
650		0	_9297890 _aMachine learning _vTextbooks.
650		0	_9297891 _aApprentissage automatique _vManual scholary
942			_0006.31 _2ddc _c6
999			_c295533 _d295533