000			01265nam a22002297a 4500
003			OSt
005			20260603113852.0
008			260603b |||||||| |||| 00| 0 eng d
020			_a9781493938438
041			_aeng
082			_a006.31 BIS-P
100			_aBishop, Christopher M.
245			_aPattern recognition and machine learning
260			_aNew York; _bSpringer; _c2009
300			_axx, 738p.
520			_aComprehensive introduction to pattern recognition and machine learning based on probabilistic methods and statistical modelling. Covers supervised and unsupervised learning, probability theory, Bayesian inference, graphical models, neural networks, kernel methods, support vector machines, mixture models, latent variable models, and approximate inference techniques. Emphasizes mathematical foundations and practical algorithms for data analysis, prediction, classification, clustering, and intelligent decision-making systems. Widely used as a graduate-level textbook and reference in machine learning, artificial intelligence, data science, and pattern recognition.
650			_aArtificial intelligence. _981212
650			_aMachine learning. _981213
650			_aPattern recognition systems. _981214
942			_cBK
999			_c200030 _d200030