the Simple Tutorial of Machine Learning

1. Linear Methods for Regression
2. Linear Methods for Classification
   1. Linear Discriminant Analysis
   2. Logistic Regression
   3. Separating Hyperplanes
3. Basis Expansions and Regularization
4. Kernel Methods
5. Model Assessment and Selection
6. Model Inference and Averaging
   1. Boostrapping
   2. EM Algorithm
   3. MCMC for Sampling fromthe Posterior
   4. Bagging
   5. Model Averaging and Stacking
   6. Stochastic Search: Bumping
7. Additive Models, Trees, and Related Methods
   1. Generalized Additive Models
   2. Tree-Based Methods
   3. MARS: Multivariate Adaptive Regression Splines
8. Boosting and Additive Trees
9. Neural Networks
10. Support Vector Machines and Flexible Discriminants
11. Prototype Methods and Nearest-Neighbors
12. Unsupervised Learning
    1. Association Rules
    2. Cluster Analysis
    3. Principal Components, Curves and Surfaces
    4. Independent Component Analysis and Exploratory Projection Pursuit
    5. Multidimensional Scaling