目次

1 Introduction.- 2 Foundations of R.- 3 Managing Data in R.- 4 Data Visualization.- 5 Linear Algebra & Matrix Computing.- 6 Dimensionality Reduction.- 7 Lazy Learning: Classification Using Nearest Neighbors.- 8 Probabilistic Learning: Classification Using Naive Bayes.- 9 Decision Tree Divide and Conquer Classification.- 10 Forecasting Numeric Data Using Regression Models.- 11 Black Box Machine-Learning Methods: Neural Networks and Support Vector Machines.- 12 Apriori Association Rules Learning.- 13 k-Means Clustering.- 14 Model Performance Assessment.- 15 Improving Model Performance.- 16 Specialized Machine Learning Topics.- 17 Variable/Feature Selection.- 18 Regularized Linear Modeling and Controlled Variable Selection.- 19 Big Longitudinal Data Analysis.- 20 Natural Language Processing/Text Mining.- 21 Prediction and Internal Statistical Cross Validation.- 22 Function Optimization.- 23 Deep Learning Neural Networks.- 24 Summary.- 25 Glossary.- 26 Index.- 27 Errata.