Index

A

• Akaike information criterion (AIC) / Laplace approximation
• allele frequencies
  • about / Beta distribution
• arm package
  • about / The arm package
• association rule mining
  • about / An overview of common machine learning tasks

B

• Bayesian averaging
  • about / Bayesian averaging
• Bayesian classification models
  • exercises / Exercises
• Bayesian inference
  • Bayesian view of uncertainty / Bayesian view of uncertainty
  • exercises / Exercises
  • for machine learning / Why Bayesian inference for machine learning?
• Bayesian information criterion (BIC) / Laplace approximation
• Bayesian logistic regression model
  • about / The Bayesian logistic regression model
  • BayesLogit R package / The BayesLogit R package
  • dataset / The dataset
  • training, preparing for / Preparation of the training and testing datasets
  • datasets testing, preparing for / Preparation of the training and testing datasets
  • using / Using the Bayesian logistic model
• Bayesian mixture models
  • about / Bayesian mixture models
  • bgmm package / The bgmm package for Bayesian mixture models
• Bayesian modeling, at Big Data scale
  • exercises / Exercises
• Bayesian models, for unsupervised learning
  • exercises / Exercises
• Bayesian neural networks
  • exercises / Exercises
• Bayesian Output Analysis Program (BOA) / R packages for Gibbs sampling
• Bayesian regression models
  • exercises / Exercises
• Bayesian theorem
  • about / Bayesian theorem
• Bayesian treatment, of neural networks
  • about / Bayesian treatment of neural networks
• Bayesian view of uncertainty
  • about / Bayesian view of uncertainty
  • prior distribution, selecting / Choosing the right prior distribution
  • posterior distribution, estimation / Estimation of posterior distribution
  • future observations, predicting / Prediction of future observations
• BayesLogit R package
  • about / The BayesLogit R package
• Beta distribution
  • about / Beta distribution
• bgmm package
  • about / Bayesian mixture models
  • fully supervised GMM / The bgmm package for Bayesian mixture models
  • semi-supervised GMM / The bgmm package for Bayesian mixture models
  • partially supervised GMM / The bgmm package for Bayesian mixture models
  • unsupervised GMM / The bgmm package for Bayesian mixture models
• bias-variance tradeoff
  • about / Model overfitting and bias-variance tradeoff
• binomial distribution
  • about / Binomial distribution
• binomlogit package / R packages for Gibbs sampling
• Bmk / R packages for Gibbs sampling
• BoomSpikeSlab package / R packages for Gibbs sampling
• brnn R package
  • about / The brnn R package

C

• CD-1 algorithm / Restricted Boltzmann machines
• CD-k algorithm / Restricted Boltzmann machines
• central limit theorem / Probability distributions
• classification
  • about / An overview of common machine learning tasks
• clustering
  • about / An overview of common machine learning tasks
• clusters, computing on cloud
  • about / Computing clusters on the cloud
  • Amazon Web Services / Amazon Web Services
  • computing instances, running on AWS / Creating and running computing instances on AWS
  • computing instances, creating / Creating and running computing instances on AWS
  • R, installing / Installing R and RStudio
  • RStudio, installing / Installing R and RStudio
  • Spark, running on EC2 / Running Spark on EC2
  • Microsoft Azure / Microsoft Azure
  • IBM Bluemix / IBM Bluemix
• common machine learning tasks
  • overview / An overview of common machine learning tasks
  • classification / An overview of common machine learning tasks
  • regression / An overview of common machine learning tasks
  • clustering / An overview of common machine learning tasks
  • association rules / An overview of common machine learning tasks
  • forecasting / An overview of common machine learning tasks
  • dimensional reduction / An overview of common machine learning tasks
  • density estimation / An overview of common machine learning tasks
• Comprehensive R Archive Network (CRAN) / Installing R and RStudio
• conditional probability
  • about / Conditional probability
• conjugate distributions / Conjugate priors
• conjugate prior for the likelihood function / Conjugate priors
• Contrastive Divergence (CD) / Restricted Boltzmann machines
• Correlated Topic Models (CTM)
  • about / The topicmodels package
• covariance
  • about / Expectations and covariance
• Cuda (C++) / Other deep learning packages in R
• curse of dimensionality
  • about / An overview of common machine learning tasks

D

• darch R package / The darch R package
• data, managing in R
  • about / Managing data in R
  • data types / Data Types in R
  • data structures / Data structures in R
  • data, importing into R / Importing data into R
  • datasets, slicing / Slicing and dicing datasets
  • datasets, dicing / Slicing and dicing datasets
  • vectorized operations / Vectorized operations
• data structures, R
  • homogeneous / Data structures in R
  • heterogeneous / Data structures in R
• data types, R
  • integer / Data Types in R
  • complex / Data Types in R
  • numeric / Data Types in R
  • character / Data Types in R
  • logical / Data Types in R
• data visualization
  • about / Data visualization
  • high-level plotting functions / High-level plotting functions
  • low-level plotting commands / Low-level plotting commands
  • interactive graphics functions / Interactive graphics functions
• DBN-DNN architecture / Deep belief networks
• deep belief nets (DBN) / Deep belief networks and deep learning
• deep belief networks
  • about / Deep belief networks and deep learning, Deep belief networks
  • restricted Boltzmann machine (RBM) / Restricted Boltzmann machines
  • darch R package / The darch R package
  • deep learning packages / Other deep learning packages in R
• deep learning
  • about / Deep belief networks and deep learning
• deepnet / Other deep learning packages in R
• density estimation
  • about / An overview of common machine learning tasks
• dimensional reduction
  • about / An overview of common machine learning tasks
• Dirichlet distribution
  • about / Dirichlet distribution
• distributed computing
  • with Hadoop / Distributed computing using Hadoop
• divergence
  • about / Variational approximation

E

• econometrics
  • about / Gamma distribution
• Elastic Computing Cloud (EC2) / Amazon Web Services
• Energy efficiency dataset
  • about / The Energy efficiency dataset
• energy function / Restricted Boltzmann machines
• Evolutionary Monte Carlo (EMC) algorithm package / R packages for the Metropolis-Hasting algorithm
• exercises
  • about / Exercises
• expectation-maximization (EM) algorithm / Bayesian mixture models
• expectations
  • about / Expectations and covariance

F

• false negative or type II error
  • about / Performance metrics for classification
• false positive or type I error
  • about / Performance metrics for classification
• foreach / Other R packages for large scale machine learning
• foreach R package / The foreach R package
• forecasting
  • about / An overview of common machine learning tasks

G

• Gamma distribution
  • about / Gamma distribution
• Gaussian mixture model (GMM) / Bayesian mixture models
• generalized linear regression
  • about / Generalized linear regression
• general purpose graphical processing units (GPGPUs) / Deep belief networks and deep learning
• ggmcmc package / R packages for Gibbs sampling
• ggplot2 / Data visualization
• ggplot2 package / Bayesian regression
• gibbs.met package
  • R packages / R packages for Gibbs sampling
• GibbsACOV package / R packages for Gibbs sampling
• Gibbs sampling
  • about / Gibbs sampling
  • R packages / R packages for Gibbs sampling
• gradient vanishing problem / Deep belief networks
• grid / Data visualization

H

• Hadoop
  • about / Distributed computing using Hadoop
• Hadoop Distributed File System (HDFS) / RHadoop for using Hadoop from R
• high-level plotting functions
  • about / High-level plotting functions

I

• IBM Bluemix
  • about / IBM Bluemix
• integrated Development environment (IDE) / Installing R and RStudio
• interactive graphics functions
  • about / Interactive graphics functions

K

• kernel density estimation (KDE)
  • about / An overview of common machine learning tasks

L

• Laplace approximation / Laplace approximation
• Latent Dirichlet allocation (LDA) / An overview of common machine learning tasks
  • about / Latent Dirichlet allocation, The lda package
  • R packages / R packages for LDA
• lattice / Data visualization
• lda package / R packages for Gibbs sampling
  • about / The lda package
• linear regression
  • using SparkR / Linear regression using SparkR
• logit function
  • using / The Bayesian logistic regression model
• loop functions, R programs
  • about / Loop functions
  • lapply / lapply
  • sapply / sapply
  • mapply / mapply
  • apply / apply
  • tapply / tapply
• low-level plotting commands
  • about / Low-level plotting commands

M

• MapReduce
  • about / Distributed computing using Hadoop
• marginal distribution
  • about / Marginal distribution
• marginalization
  • about / Marginal distribution
• Markov Chain Monte Carlo (MCMC) simulations
  • about / Monte Carlo simulations
• maximum a posteriori (MAP) estimation / Maximum a posteriori estimation
• maximum likelihood estimate / Bayesian view of uncertainty
• maximum likelihood method / Bayesian mixture models
• MCMCglm package / R packages for Gibbs sampling
• mcmc package / R packages for the Metropolis-Hasting algorithm
• Metropolis-Hasting algorithm
  • about / The Metropolis-Hasting algorithm
  • R packages / R packages for the Metropolis-Hasting algorithm
• MHadaptive / R packages for the Metropolis-Hasting algorithm
• Microsoft Azure
  • about / Microsoft Azure
• miles per gallon (mpg) / Exercises
• mixed membership stochastic block model (MMSB)
  • about / The lda package
• model overfitting
  • about / Model overfitting and bias-variance tradeoff
• model regularization
  • about / Model regularization
  • Ridge regression / Model regularization
  • Lasso / Model regularization
• models selection
  • about / Selecting models of optimum complexity
  • subset selection / Subset selection
  • model regularization / Model regularization
• Monte Carlo simulations
  • about / Monte Carlo simulations
  • Metropolis-Hasting algorithm / The Metropolis-Hasting algorithm
  • Gibbs sampling / Gibbs sampling
• multicore / Other R packages for large scale machine learning

N

• Naïve Bayes classifier
  • about / The Naïve Bayes classifier
  • text processing, with tm package / Text processing using the tm package
  • model training and prediction / Model training and prediction

O

• OpenBUGS MCMC package / R packages for Gibbs sampling
• Open Database Connectivity (ODBC) / Importing data into R

P

• parallel / Other R packages for large scale machine learning
• parallel R package / The parallel R package
• partially supervised GMM
  • belief( ) function / The bgmm package for Bayesian mixture models
  • soft( ) function / The bgmm package for Bayesian mixture models
• partition function / Restricted Boltzmann machines
• PCorpus (permanent corpus) / Text processing using the tm package
• performance metrics, for classification
  • about / Performance metrics for classification
• Pig
  • about / Distributed computing using Hadoop
• posterior probability distribution
  • about / Bayesian view of uncertainty, Estimation of posterior distribution
  • estimation / Estimation of posterior distribution
  • maximum a posteriori (MAP) estimation / Maximum a posteriori estimation
  • Laplace approximation / Laplace approximation
  • Monte Carlo simulations / Monte Carlo simulations
  • variational approximation / Variational approximation
  • simulating / Simulation of the posterior distribution
• prior probability distribution
  • about / Bayesian view of uncertainty
  • selecting / Choosing the right prior distribution
  • non-informative priors / Non-informative priors
  • subjective priors / Subjective priors
  • conjugate priors / Conjugate priors
  • hierarchical priors / Hierarchical priors
• probability distributions
  • about / Probability distributions
  • probability mass function (pmf) / Probability distributions
  • categorical distribution / Probability distributions
  • probability density function (pdf) / Probability distributions
  • binomial distribution / Binomial distribution
  • Beta distribution / Beta distribution
  • Gamma distribution / Gamma distribution
  • Dirichlet distribution / Dirichlet distribution
  • Wishart distribution / Wishart distribution

R

• R
  • installing / Installing R and RStudio, Installing R and RStudio
  • program, writing / Your first R program
  • data, managing / Managing data in R
• RBugs / R packages for Gibbs sampling
• RcppDL / Other deep learning packages in R
• regression
  • about / An overview of common machine learning tasks
• regression of energy efficiency, with building parameters
  • about / Regression of energy efficiency with building parameters
  • ordinary regression / Ordinary regression
  • Bayesian regression / Bayesian regression
• R environment
  • setting up / Setting up the R environment and packages
  • exercises / Exercises
• Resilient Distributed Datasets (RDD)
  • about / Spark – in-memory distributed computing
• restricted Boltzmann machine (RBM) / Restricted Boltzmann machines
• Reuter_50_50 dataset
  • about / The topicmodels package
• RHadoop
  • about / RHadoop for using Hadoop from R
  • for using Hadoop from R / RHadoop for using Hadoop from R
  • rhdfs package / RHadoop for using Hadoop from R
  • rhbase package / RHadoop for using Hadoop from R
  • plyrmr package / RHadoop for using Hadoop from R
  • rmr2 package / RHadoop for using Hadoop from R
• risk modeling / Subjective priors
• Rmpi / Other R packages for large scale machine learning
• ROC curve
  • about / Performance metrics for classification
• RODBC package
  • about / Importing data into R
  • functions / Importing data into R
• Root Mean Square Error (RMSE) / Exercises
• R package e1071
  • about / The Naïve Bayes classifier
• R packages
  • about / Setting up the R environment and packages
• R packages, for large scale machine learning
  • about / Other R packages for large scale machine learning
  • parallel R package / The parallel R package
  • foreach R package / The foreach R package
• R packages, for LDA
  • about / R packages for LDA
  • topicmodels package / The topicmodels package
  • lda package / The lda package
• R programs
  • writing / Writing R programs
  • control structures / Control structures
  • functions / Functions
  • scoping rules / Scoping rules
  • loop functions / Loop functions
• RStudio
  • about / Setting up the R environment and packages
  • URL / Installing R and RStudio
  • installing / Installing R and RStudio, Installing R and RStudio

S

• SamplerCompare package / R packages for Gibbs sampling
• sampling
  • about / Sampling
  • random uniform sampling, from interval / Random uniform sampling from an interval
  • from normal distribution / Sampling from normal distribution
• sigmoid function
  • about / Two-layer neural networks
• Simple Storage Service (S3) / Amazon Web Services
• snow / Other R packages for large scale machine learning
• SnowballC package
  • about / Text processing using the tm package
• softmax
  • about / Two-layer neural networks
• Spark
  • about / Spark – in-memory distributed computing
  • URL / Spark – in-memory distributed computing
  • running, on EC2 / Running Spark on EC2
• SparkR
  • about / SparkR
• stocc package / R packages for Gibbs sampling
• subsets, of R objects
  • Single bracket [ ] / Slicing and dicing datasets
  • Double bracket [[ ]] / Slicing and dicing datasets
  • Dollar sign $ / Slicing and dicing datasets
  • use of negative index values / Slicing and dicing datasets
• subset selection approach
  • about / Subset selection
  • forward selection / Subset selection
  • backward selection / Subset selection
• supervised LDA (sLDA)
  • about / The lda package
• support vector machines (SVM) / An overview of common machine learning tasks

T

• Theano / Other deep learning packages in R
• tm package
  • about / Text processing using the tm package
• topic modeling, with Bayesian inference
  • about / Topic modeling using Bayesian inference
  • Latent Dirichlet allocation / Latent Dirichlet allocation
• topicmodels package
  • about / The topicmodels package
• two-layer neural networks
  • about / Two-layer neural networks

U

• Unsupervised( ) function
  • about / The bgmm package for Bayesian mixture models
  • parameters / The bgmm package for Bayesian mixture models

V

• variational approximation
  • about / Variational approximation
• variational calculus problem
  • about / Variational approximation
• vbdm package
  • about / Variational approximation
• VBmix package
  • about / Variational approximation
• vbsr package
  • about / Variational approximation
• VCorpus (volatile corpus) / Text processing using the tm package

W

• Wishart distribution
  • about / Wishart distribution
• word error rate / Deep belief networks and deep learning