# Obviously Awesome

ESL is one of the most widely accepted introductory texts on Machine Learning. Let's just leave it at that. Each chapter link points to a PDF of the relevant book's section. Introduction Supervised Learning Variable Types
Least Squares & Nearest Neighbors
Decision Theory
Statistical Models
Regression Models
Estimator Classes
Model Selection & Bias-Variance

Linear Regression Least Squares
Subsets
Shrinkage
Derived Input Directions
Comparisons
Multiple Outcomes
Lasso & Related
Computational Factors

Linear Classification Intro
Indicator Matrix
Discriminant Analysis
Logistic Regression
Separating Hyperplanes

Basis Expansions & Regularization Intro
Piecewise Polynomials & Splines
Filtering & Feature Extraction
Smoothing Splines
Auto-Selection of Smoothing Parameters
Non-parametric Logistic Regression
Multi-dimensional Splines
Regularization & Reproducing Kernel Hilbert Spaces
Wavelet Smoothing

Kernel Smoothing 1D Smoothers
Kernel Width
Local Regression
Structured Local Regression
Local Likelihood
Kernel Density Estimation
Mixture Models
Computational Factors

Model Assessment Intro
Bias, Variance, Model Complexity
Bias-Variance Decomposition
Training Error Rates & Optimism
Effective # of Parameters
Bayesian Approach & BIC
Minimum Description Length
Vapnik-Chervonenkis Dimension
Cross Validation
Bootstrap Methods

Model Inference & Averaging Intro
Bootstrap & Max Likelihood
Bayesian Methods
Bootstrap:Bayesian Relation
EM Algorithm
MCMC for Posterior Sampling
Bagging
Model Averaging & Stacking
Bumping

Additive Models Generalized Additive Models
Tree-Based Methods
PRIM
MARS
Hierarchical Expert Mixtures
Missing Data
Computational Factors

Boosting & Additive Trees Boosting Methods
Why Exponential Loss?
Loss Functions
"Off the Shelf" Procedures
Example: Spam Data
Boosting Trees
Right-Sized Trees
Regularization
Interpretation
Examples

Neural Nets Intro
Projection Persuit Regression
Neural Nets
Fitting
Training Issues
Examples
Discussion
Bayesian NNs
Computational Factors

SVMs & Flexible Discriminants Intro
Support Vector Classifier
Support Vector Machines & Kernels
Generalizing Linear Discriminant Analysis
Flexible Discriminant Analysis
Penalized Discriminant Analysis
Mixture Discriminant Analysis

Prototypes & Nearest Neighbors Intro
Prototypes (K-Means, LVQ, Gaussian Mixtures)
k-Nearest-Neighbor Classifiers
Computational Factors

Unsupervised Learning Intro
Association Rules
Cluster Analysis
Self-Organizing Maps
Principal Components
Non-Negative Matrix Factorization
Independent Component Analysis
Multidimensional Scaling (MDS)
Non-Linear Dimension Reduction

Random Forests Intro
Definition
Out-of-Bag, Variable Importance, Proximity, Overfitting
Analysis

Ensembles Intro
Boosting & Regularization
Learning & Ensembles

Graphs (Undirected) Intro
Markov Graphs
Continuous-Variable Graphs
Discrete-Variable Graphs

Dimensionality When P >> N
Diagonal LDA