000			01018nam a22001697a 4500
999			_c1328 _d1328
005			20211208135012.0
008			211208b ||||| |||| 00| 0 eng d
020			_a9781493938438
082			_a006.4 _bBIS
100			_a Bishop, Christopher M.
245			_a Pattern recognition and machine learning _c Christopher M Bishop
260			_aNew York : _bSpringer, _c ©2006.
300			_a738 pages
505			_t Probability Distributions.- _t Linear Models for Regression.- _t Linear Models for Classification.- _tNeural Networks.- . _tKernel Methods.- _tSparse Kernel Machines.- _tGraphical Models.- _t Mixture Models and EM.- _tApproximate Inference.- _tSampling Methods.- _tContinuous Latent Variables.- _tSequential Data.- _tCombining Models
520			_aFamiliarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
942			_2ddc _cBK