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NOTE: Chapters are indexed as follows

Ch. 1 - Statistical Learning Theory

Ch. 2 - Local Methods

Ch. 3 - Bias Variance and Cross-Validation

Ch. 4 - Regularized Least Squares

Ch. 5 - Regularized Least Squares Classification

Ch. 6 - Feature, Kernels and Representer Theorem

Ch. 7 - Regularization Networks

Ch. 8 - Logistic Regression

Ch. 9 - From Perceptron to SVM

Ch. 10 - Dimensionality Reduction

Ch. 11 - Variable Selection

Ch. 12 - A Glimpse Beyond The Fence

Use Ch. 0 for Appendix: Mathematical Tools

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