Linear models. Linear regression.

Linear discriminant analysis. Logistic

regression. Gradient descent.

Stochastic gradient descent.

The machine learning approach.

Cost functions. Empirical risk

minimization. Maximum likelihood

estimation. Model evaluation. Crossvalidation.

Feature selection. Search-based

feature selection. Regularization.

Tree-based models. Decision trees.

Random forest. Bagging. Gradient

tree boosting.

Clustering. k-means. Expectation

Maximization.

Non-linear regression. Basis

functions. Splines. Support vector

machines. Kernel trick.

Neural networks. Perceptron.

Activation functions.

Backpropagation.