Learning Bayesian models with R : become an expert in Bayesian machine learning methods using R and apply them to solve real-world big data problems

Learning Bayesian models with R become an expert in Bayesian machine learning methods using R and apply them to solve real-world big data problems

Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems About This Book Understand the principles of Bayesian Inference with less mathematical equations Learn state-of-the art Machine Learning methods Familiarize yourself with the recent adv...

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Detalles Bibliográficos
Otros Autores: Koduvely, Hari M., author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham : Packt Publishing 2015.
Edición:1st edition
Colección:Community experience distilled.
Materias:
Machine learning.
Bayesian statistical decision theory.
Neural networks (Computer science).
R (Computer program language).
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630003206719
Tabla de Contenidos:
  • Cover
  • Copyright
  • Credits
  • About the Author
  • About the Reviewers
  • www.PacktPub.com
  • Table of Contents
  • Preface
  • Chapter 1: Introducing the Probability Theory
  • Probability distributions
  • Conditional probability
  • Bayesian theorem
  • Marginal distribution
  • Expectations and covariance
  • Binomial distribution
  • Beta distribution
  • Gamma distribution
  • Dirichlet distribution
  • Wishart distribution
  • Exercises
  • References
  • Summary
  • Chapter 2: The R Environment
  • Setting up the R environment and packages
  • Installing R and RStudio
  • Your first R program
  • Managing data in R
  • Data Types in R
  • Data structures in R
  • Importing data into R
  • Slicing and dicing datasets
  • Vectorized operations
  • Writing R programs
  • Control structures
  • Functions
  • Scoping rules
  • Loop functions
  • lapply
  • sapply
  • mapply
  • apply
  • tapply
  • Data visualization
  • High-level plotting functions
  • Low-level plotting commands
  • Interactive graphics functions
  • Sampling
  • Random uniform sampling from an interval
  • Sampling from normal distribution
  • Exercises
  • References
  • Summary
  • Chapter 3: Introducing Bayesian Inference
  • Bayesian view of uncertainty
  • Choosing the right prior distribution
  • Non-informative priors
  • Subjective priors
  • Conjugate priors
  • Hierarchical priors
  • Estimation of posterior distribution
  • Maximum a posteriori estimation
  • Laplace approximation
  • Monte Carlo simulations
  • Variational approximation
  • Prediction of future observations
  • Exercises
  • References
  • Summary
  • Chapter 4: Machine Learning Using Bayesian Inference
  • Why Bayesian inference for machine learning?
  • Model overfitting and bias-variance tradeoff
  • Selecting models of optimum complexity
  • Subset selection
  • Model regularization
  • Bayesian averaging
  • An overview of common machine learning tasks.
  • References
  • Summary
  • Chapter 5: Bayesian Regression Models
  • Generalized linear regression
  • The arm package
  • The Energy efficiency dataset
  • Regression of energy efficiency with building parameters
  • Ordinary regression
  • Bayesian regression
  • Simulation of the posterior distribution
  • Exercises
  • References
  • Summary
  • Chapter 6: Bayesian Classification Models
  • Performance metrics for classification
  • The Naïve Bayes classifier
  • Text processing using the tm package
  • Model training and prediction
  • The Bayesian logistic regression model
  • The BayesLogit R package
  • The dataset
  • Preparation of the training and testing datasets
  • Using the Bayesian logistic model
  • Exercises
  • References
  • Summary
  • Chapter 7: Bayesian Models for Unsupervised Learning
  • Bayesian mixture models
  • The bgmm package for Bayesian mixture models
  • Topic modeling using Bayesian inference
  • Latent Dirichlet allocation
  • R packages for LDA
  • The topicmodels package
  • The lda package
  • Exercises
  • References
  • Summary
  • Chapter 8: Bayesian Neural Networks
  • Two-layer neural networks
  • Bayesian treatment of neural networks
  • The brnn R package
  • Deep belief networks and deep learning
  • Restricted Boltzmann machines
  • Deep belief networks
  • The darch R package
  • Other deep learning packages in R
  • Exercises
  • References
  • Summary
  • Chapter 9: Bayesian Modeling at Big Data Scale
  • Distributed computing using Hadoop
  • RHadoop for using Hadoop from R
  • Spark - in-memory distributed computing
  • SparkR
  • Linear regression using SparkR
  • Computing clusters on the cloud
  • Amazon Web Services
  • Creating and running computing instances on AWS
  • Installing R and RStudio
  • Running Spark on EC2
  • Microsoft Azure
  • IBM Bluemix
  • Other R packages for large scale machine learning
  • The parallel R package.
  • The foreach R package
  • Exercises
  • References
  • Summary
  • Index.

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