Machine Learning
- Introduction to Machine Learning
- Regression
- bias-variance tradeoff
- regularization (ridge regression)
- non-quadratic regularization (lasso)
- naive bayes
- logistic regression
- perceptron
- support vector machine
- decision trees and random forests
- kernels, bootstrap
- boosting
- Unsupervised Learning
- K-means
- PCA (and autoencoder)
- EM, Non-parametric methods
- neural network
- deep learing
- Reinforcement Learning