Pattern recognition and machine learning

By:

Bishop, Christopher M

Material type: Text

TextLanguage: English Publication details: New York; Springer; 2009Description: xx, 738pISBN:

9781493938438

Subject(s):

DDC classification:

006.31 BIS-P

Summary: Comprehensive 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.

Item type: Books and Monographs List(s) this item appears in: List of New Arrivals (Books)

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Holdings ( 3 )
Title notes ( 1 )

Holdings
Item type	Current library	Home library	Collection	Call number	Status	Barcode
Books and Monographs	Central Library, NIT Jalandhar General Stacks	Central Library, NIT Jalandhar	Center for Artificial Intelligence	006.31 BIS-P (Browse shelf(Opens below))	Available	102886
Books and Monographs	Central Library, NIT Jalandhar General Stacks	Central Library, NIT Jalandhar	Center for Artificial Intelligence	006.31 BIS-P (Browse shelf(Opens below))	Available	102887
Books and Monographs	Central Library, NIT Jalandhar General Stacks	Central Library, NIT Jalandhar	Center for Artificial Intelligence	006.31 BIS-P (Browse shelf(Opens below))	Available	102888

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Comprehensive 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.

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