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Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) 🔍
Murphy, Kevin P., 1970- author Cambridge, Mass. : MIT Press, MIT Press, Cambridge, Mass, 2012
English [en] · PDF · 55.2MB · 2012 · 📗 Book (unknown) · 🚀/duxiu/ia/zlib · Save
description
1 online resource (xxix, 1067 pages) :
"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
Includes bibliographical references and indexes
Contents -- Preface -- 1 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
Online resource; title from PDF title page (JSTOR, viewed October 20, 2016)
Alternative filename
ia/machinelearningp0000murp.pdf
Alternative author
Kevin P. Murphy, Kevin P. Murphy
Alternative publisher
THE MIT PRESS
Alternative publisher
AAAI Press
Alternative edition
Adaptive computation and machine learning, Cambridge, MA, London, United States, 2012
Alternative edition
Adaptive computation and machine learning series, Cambridge, MA, Massachusetts, 2012
Alternative edition
Adaptive computation and machine learning series, Cambridge, Mass, c2012
Alternative edition
United States, United States of America
Alternative edition
Illustrated, PT, 2012
Alternative edition
4, 2012
metadata comments
Includes bibliographical references and index.
metadata comments
Includes bibliographical references (p. [1015]-1045) and indexes.
Имеется микрофильм Москва Российская государственная библиотека 2014 черно-белый, галогенидосеребр., безопасная основа, 1 рулон, 35 мм, норм. кратность
metadata comments
РГБ
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Alternative description
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. Probability -- Generative Models For Discrete Data -- Gaussian Models -- Bayesian Statistics -- Frequentist Statistics -- Linear Regression -- Logistic Regression -- Generalized Linear Models And The Exponential Family -- Directed Graphical Models (bayes Nets) -- Mixture Models And The Em Algorithm -- Latent Linear Models -- Sparse Linear Models -- Kernels -- Gaussian Processes -- Adaptive Basis Function Models -- Markov And Hidden Markov Models -- State Space Models -- Undirected Graphical Models (markov Random Fields) -- Exact Inference For Graphical Models -- Variational Inference -- More Variational Inference -- Monte Carlo Inference -- Markov Chain Monte Carlo (mcmc) Inference -- Clustering -- Graphical Model Structure Learning -- Latent Variable Models For Discrete Data -- Deep Learning -- Notation. Kevin P. Murphy. Includes Bibliographical References And Index.
Alternative description
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. 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. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Alternative description
Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. 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 illustrated with images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. 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
date open sourced
2023-06-28
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