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- 01-Introduction
- 02-Overview-of-Supervised-Learning
- 03-Linear-Methods-for-Regression
- 04-Linear-Methods-for-Classification
- 05-Basis-Expansions-and-Regularization
- 06-Kernel-Smoothing-Methods
- 07-Model-Assessment-and-Selection
- 08-Model-Inference-and-Averaging
- 09-Additive-Models-Trees-and-Related-Methods
- 10-Boosting-and-Additive-Trees
- 11-Neural-Networks
- 12-Support-Vector-Machines-and-Flexible-Discriminants
- 13-Prototype-Methods-and-Nearest-Neighbors
- 14-Unsupervised-Learning
- 15-Random-Forests
- 16-Ensemble-Learning
- 17-Undirected-Graphical-Models
- 18-High-Dimensional-Problems
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