000			02837nam a22002297a 4500
999			_c51208 _d52755
003			ISURa
008			190110b xxu||||| |||| 00| 0 eng d
020			_a9780262018029
041			_aEnglish
082			_a006.31 _bMUR
100			_a Murphy , Kevin P _969131
245			_aMachine learning _b a probabilistic perspective
260			_aCambridge, Mass _bMIT Press, _c2012.
300			_a xxix , 1071 _bsome col. _c23 cm.
440			_aAdaptive computation and machine learning. _969132
500			_aContents Preface1 Introduction 2 Probability-3 Generative Models for Discrete Data- "4 Gaussian Models""; ""5 Bayesian Statistics""; ""6 Frequentist Statistics""; ""7 Linear Regression""; ""8 Logistic Regression""; ""9 Generalized Linear Models and the Exponential Family""; ""10 Directed Graphical Models (Bayes Nets)""; ""11 Mixture Models and the EM Algorithm""; ""12 Latent Linear Models""; ""13 Sparse Linear Models""; ""14 Kernels""; ""15 Gaussian Processes""; ""16 Adaptive Basis Function Models""; ""17 Markov and Hidden Markov Models""; ""18 State Space Models"" ""19 Undirected Graphical Models (Markov Random Fields)""""20 Exact Inference for Graphical Models""; ""21 Variational Inference""; ""22 More Variational Inference""; ""23 Monte Carlo Inference""; ""24 Markov Chain Monte Carlo (MCMC) Inference""; ""25 Clustering""; ""26 Graphical Model Structure Learning""; ""27 Latent Variable Models for Discrete Data""; ""28 Deep Learning""; ""Notation""; ""Bibliography""; ""Index to Code""; ""Index to Keywords""
520			_a "This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online"--Back cover.
650			_aMachine learning. _969133
650			_a Probabilities. _9675
650			_a COMPUTERS -- Enterprise Applications -- Business Intelligence Tools. _969134
942			_2ddc _cLN