000			01854nam a22003137a 4500
003			OSt
005			20231013222915.0
008			201228b ||||| |||| 00| 0 eng d
020			_a978-14-8738484 (Hardback)
040			_aCvSU-CCAT Campus Library. _bEnglish. _cCvSU-CCAT Campus Library. _erda
050			_aCIR Q 325.5 _bR64 2017
100			_aRogers, Simon, author. _9582
245			_aA first course in machine learning / _cSimon Rogers; Mark Girolami.
250			_aSecond edition
260			_aBoca Raton : _bCRC Press, _cc2017.
300			_axxix, 397 pages : _billustrations (black and white) ; _c24 cm.
490			_aChapman & Hall/CRC machine learning & pattern recognition series
500			_aIncludes e-book access. Previous edition: 2012.
501			_a"A Chapman & Hall book."
504			_aIncludes bibliographical references and index.
505			_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. Further Topics in Markov Chain Monte Carlo. Classification and Regression with Gaussian Processes. Dirichlet Process models.
520			_aThe new edition of this popular, undergraduate textbook has been revised and updated to reflect current growth areas in Machine Learning. The new edition includes three new chapters with more detailed discussion of Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models.
546			_aIn English text.
650			_aMachine learning. _9301
650			_aComputers and IT. _9583
700			_aGirolami, Mark, author. _9584
942			_cBK _eSecond edition. _hQ 325.5 R64 2017 _kCIR _2lcc
999			_c222 _d222