Revision

Supervised Learning

Linear Regression

Logistic Regression

Decision Tree

Random Forest

Boosting

SVM

Gaussian Discriminant Analysis

Naive Bayes

Unsupervised Learning

K-means

Mean shift

Hierarchical clustering

DBSCAN

OPTICS

Spectral clustering

PCA

Other Resources

Loss functions

Estimators

Metrics

Sigmoid and Softmax

Grid and Random Search

Cross validation

EM Algorithm

Vapnik-Chervonenkis dimension

Bias variance tradeoff

Unbalanced Dataset

Interpretability