Table of Contents

1. copyright
2. foreword
3. preface
4. acknowledgments
5. Background
1. Introduction
1. Machine Learning
2. Problem Domains
3. Considerations
4. Book Organization
5. Further Reading
6. Algorithms
1. Optimization
1. Golden Section Search
2. Nelder-Mead Method
3. Gradient Descent
4. Conjugate Gradient Method
5. BFGS Method
2. Regression
1. Ordinary Least Squares Regression
2. Logistic Regression
3. Stepwise Regression
4. Multivariate Adaptive Regression Splines
5. Locally Estimated Scatterplot Smoothing
3. Regularization
1. Ridge Regression
2. Least Absolute Shrinkage and Selection Operator
3. Elastic Net
4. Discriminant Function Analysis
1. Linear Discriminant Analysis
2. Quadratic Discriminant Analysis
3. Flexible Discriminant Analysis
4. Mixture Discriminant Analysis
5. Decision Trees
1. Classification And Regression Tree
2. C4.5
6. Kernel Machines
1. Support Vector Machines
7. Bayesian
1. Naive Bayes
2. Averaged One-Dependence Estimators
8. Lazy Learning
1. K-Nearest Neighbor
9. Ensembles
1. Bootstrapped Aggregation
2. AdaBoost
3. Gradient Boosting
4. Random Forest
10. Clustering and Mixture Models
1. K-means
2. Expectation Maximization
11. Dimensionality Reduction
1. Principle Component Analysis
2. Partial Least Squares Regression
3. Sammon Mapping
4. Multidimensional Scaling
5. Projection Pursuit
7. Extensions
1. Advanced Topics
1. Plotting
2. Statistics
3. Model Tuning
4. Model Verification
8. Appendix A - R: Quick-Start Guide
1. Overview
2. Language Basics
3. R Idioms
9. Errata