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docs/01-Introduction/1.1-Introduction.md

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docs/02-Overview-of-Supervised-Learning/2.1-Introduction.md

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docs/02-Overview-of-Supervised-Learning/2.2-Variable-Types-and-Terminology.md

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docs/02-Overview-of-Supervised-Learning/2.3-Two-Simple-Approaches-to-Prediction.md

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docs/02-Overview-of-Supervised-Learning/2.4-Statistical-Decision-Theory.md

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docs/02-Overview-of-Supervised-Learning/2.5-Local-Methods-in-High-Dimensions.md

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docs/02-Overview-of-Supervised-Learning/2.6-Statistical-Models-Supervised-Learning-and-Function-Approximation.md

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docs/02-Overview-of-Supervised-Learning/2.7-Structured-Regression-Models.md

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docs/02-Overview-of-Supervised-Learning/2.8-Classes-of-Restricted-Estimators.md

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docs/02-Overview-of-Supervised-Learning/2.9-Model-Selection-and-the-Bias-Variance-Tradeoff.md

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docs/02-Overview-of-Supervised-Learning/Bibliographic-Notes.md

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docs/03-Linear-Methods-for-Regression/3.1-Introduction.md

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docs/03-Linear-Methods-for-Regression/3.2-Linear-Regression-Models-and-Least-Squares.md

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docs/03-Linear-Methods-for-Regression/3.3-Subset-Selection.md

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docs/03-Linear-Methods-for-Regression/3.4-Shrinkage-Methods.md

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docs/03-Linear-Methods-for-Regression/3.5-Methods-Using-Derived-Input-Directions.md

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docs/03-Linear-Methods-for-Regression/3.6-A-Comparison-of-the-Selection-and-Shrinkage-Methods.md

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docs/03-Linear-Methods-for-Regression/3.7-Multiple-Outcome-Shrinkage-and-Selection.md

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docs/03-Linear-Methods-for-Regression/3.8-More-on-the-Lasso-and-Related-Path-Algorithms.md

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docs/03-Linear-Methods-for-Regression/3.9-Computational-Considerations.md

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docs/03-Linear-Methods-for-Regression/Bibliographic-Notes.md

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docs/04-Linear-Methods-for-Classification/4.1-Introduction.md

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docs/04-Linear-Methods-for-Classification/4.2-Linear-Regression-of-an-Indicator-Matrix.md

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docs/04-Linear-Methods-for-Classification/4.3-Linear-Discriminant-Analysis.md

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docs/04-Linear-Methods-for-Classification/4.4-Logistic-Regression.md

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docs/04-Linear-Methods-for-Classification/4.5-Separating-Hyperplanes.md

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docs/04-Linear-Methods-for-Classification/Bibliographic-Notes.md

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docs/05-Basis-Expansions-and-Regularization/5.1-Introduction.md

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docs/05-Basis-Expansions-and-Regularization/5.2-Piecewise-Polynomials-and-Splines.md

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docs/05-Basis-Expansions-and-Regularization/5.3-Filtering-and-Feature-Extraction.md

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docs/05-Basis-Expansions-and-Regularization/5.4-Smoothing-Splines.md

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docs/05-Basis-Expansions-and-Regularization/5.5-Automatic-Selection-of-the-Smoothing-Parameters.md

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docs/05-Basis-Expansions-and-Regularization/5.6-Nonparametric-Logistic-Regression.md

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docs/05-Basis-Expansions-and-Regularization/5.7-Multidimensional-Splines.md

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docs/05-Basis-Expansions-and-Regularization/5.8-Regularization-and-Reproducing-Kernel-Hibert-Spaces.md

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docs/05-Basis-Expansions-and-Regularization/5.9-Wavelet-Smoothing.md

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docs/05-Basis-Expansions-and-Regularization/Appendix-Computations-for-B-splines.md

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docs/05-Basis-Expansions-and-Regularization/Bibliographic-Notes.md

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docs/06-Kernel-Smoothing-Methods/6.0-Introduction.md

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docs/06-Kernel-Smoothing-Methods/6.1-One-Dimensional-Kernel-Smoothers.md

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docs/06-Kernel-Smoothing-Methods/6.2-Selecting-the-Width-of-the-Kernel.md

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docs/06-Kernel-Smoothing-Methods/6.3-Local-Regression-in-Rp.md

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docs/06-Kernel-Smoothing-Methods/6.4-Structured-Local-Regression-Models-in-Rp.md

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docs/06-Kernel-Smoothing-Methods/6.5-Local-Likelihood-and-Other-Models.md

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docs/06-Kernel-Smoothing-Methods/6.6-Kernel-Density-Estimation-and-Classification.md

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docs/06-Kernel-Smoothing-Methods/6.7-Radial-Basis-Functions-and-Kernels.md

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