000			01454cam a2200253 a 4500
008			120222s2011 flu b 001 0 eng
010			_a2011039389
020			_a9781439824146
035			_a(Sirsi) u8012
040			_aEG-CaNU _c EG-CaNU _d EG-CaNU
042			_ancode
082	0	0	_a006.31 _2 23
100	1		_aRogers, Simon, _d 1979- _914491
245	1	2	_aA first course in machine learning / _c Simon Rogers, Mark Girolami.
260			_aBoca Raton : _b CRC Press, _c 2011.
300			_axx, 285 p. ; _c 23 cm.
490	0		_aMachine learning & pattern recognition series
504			_aIncludes bibliographical references and index.
505	0		_aLinear Modelling: A Least Squares Approach -- Linear Modelling: A Maximum Likelihood Approach -- The Bayesian Approach to Machine Learning -- Bayesian Inference -- Classification -- Clustering -- Principal Components Analysis and Latent Variable Models.
520			_aA First Course in Machine Learning covers the core mathematical and statistical techniques needed to understand some of the most popular machine learning algorithms. The algorithms presented span the main problem areas within machine learning: classification, clustering and projection. The text gives detailed descriptions and derivations for a small number of algorithms rather than cover many algorithms in less detail.
650		0	_aMachine learning. _914492
700	1		_aGirolami, Mark, _d 1963- _914493
596			_a1
999			_c6915 _d6915