v0.1

1. Supervised Learning

a) Classification
Nearest Neighbors
Naive Bayes
Perceptron
Linear Support Vector Machines
Logistic Regression
Maximum Entropy
Decision Trees
Random Forest
Feed-forward Neural Network
AdaBoost

b) Regression
Nearest Neighbors
Linear Regression
Decision Trees
Random Forest
Feed-forward Neural Network
Gradient Boosting

c) Ranking
RankNet

2. Unsupervised Learning

a) Clustering:
K-Means

b) Decomposition:
PCA