000			01678cam a22003017a 4500
001			17609682
005			20160108155153.0
008			130131s2012 enka b 001 0 eng d
010			_a 2012289353
015			_aGBB254843 _2bnb
016	7		_a016098961 _2Uk
020			_a9781107422223 (pbk.)
035			_a(OCoLC)ocn795181906
042			_alccopycat
050	0	0	_aQ325.5 _b.F53 2012
082	0	4	_a006.31 FLA/Mac _223
100	1		_aFlach, Peter A.
245	1	0	_aMachine learning :the art and science of algorithms that make sense of data _cPeter Flach.
260			_aNew York : _bCambridge University Press, _c2015
300			_axvii, 396 p. : _bcol. ill. ; _c24 cm.
504			_aIncludes bibliographical references and index.
505	0		_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	3		_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	_aMachine learning _vTextbooks.
650		7	_aApprentissage automatique _xManuels scolaires. _2ram
906			_a7 _bcbc _ccopycat _d2 _encip _f20 _gy-gencatlg
955			_bxe10 2013-01-31 z-processor 2 copies to USPL _ixh14 2013-02-06 to BCCD
999			_c119522 _d119522