Machine Learning for Data Science 1

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. Cross-validation.

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.