000			01673nam a2200289Ia 4500
003			BD-DhDIU
005			20230822151128.0
008			041027s2004 maua b 001 0 eng d
010			_a 2004109627
020			_a0262012111
035			_a(OCoLC)ocm56830710
035			_a(NNC)5003624
040			_aTEF _cTEF _dOrLoB-B _dBD-DhDIU _bENG
050		4	_aQ325.5 _b.A473 2004
082	0	4	_a006.31/ A456i _222
100	1		_aAlpaydin, Ethem. _93071
245	1	0	_aIntroduction to machine learning _cEthem Alpaydın.
260			_aNew Delhi : _bPrentice-Hall of India, _cc2004. [reprinted 2006]
300			_axxx, 415 p. : _bill. , figs.; _c24 cm.
504			_aIncludes bibliographical references and index.
505	0	0	_g1. _tIntroduction -- _g2. _tSupervised learning -- _g3. _tBayesian decision theory -- _g4. _tParametric methods -- _g5. _tMultivariate methods -- _g6. _tDimensionality reduction -- _g7. _tClustering -- _g8. _tNonparametric methods -- _g9. _tDecision trees -- _g10. _tLinear discrimination -- _g11. _tMultilayer perceptrons -- _g12. _tLocal models -- _g13. _tHidden Markov models -- _g14. _tAssessing and comparing classification algorithms -- _g15. _tCombining multiple learners -- _g16. _tReinforcement learning -- _gA. _tProbability.
520	1		_a"This book can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods."--BOOK JACKET.
526			_aCSE, CIS, CS
650		0	_aMachine learning. _93072
900			_bTOC
942			_2ddc _cBK _026
999			_c2233 _d2233