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      • [PDF] Machine Learning: A Probabilistic Perspective Free Download by Kevin P. Murphy | Publisher : The MIT Press | Category : Computers & Internet | Tags : Electronic, Patterns, Algorithms, Biology, Graphical, College | ISBN-10 : 0262018020 | ISBN-13 : 9780262018029
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      • Machine Learning: A Probabilistic Perspective by Kevin P. Murphy, Publisher : The MIT Press
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      • 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.

        Table of Contents

        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

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    • Book Details
      • Book Name : Machine Learning: A Probabilistic Perspective

        Edition : 1

        Author : Kevin P. Murphy

        Publisher : The MIT Press

        Category : Computers & Internet

        ISBN-10 : 0262018020

        ISBN-13 : 9780262018029

        ASIN : 0262018020

        Pages : 1104

        Language : English

        Publish Date : August 24, 2012
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