Become familiar with different analytical tools, based on data, to make business decisions Develop skills to analyze and find relationships between many variables/features Relax some of the assumptions in classical linear regression Deal with the curse of dimensionality in high-dimensional problems Acquire knowledge about the main tools in advanced predictive tools and handle the R language with those models

1. Introduction. 1.1. Motivating examples. 1.2. Linear regression: a brief review. 1.3. Extensions of linear models. 2. Non-linear relationships. 2.1. Introduction. 2.2. Tranformations. 2.3. Interactions. 2.4. Polynomial regression. 2.5. Non-linear regression models. 3. Generalized regression models. 3.1. Introduction. 3.2. Model formulation and estimation. 3.3. Inference for model parameters. 3.4. Model selection. 3.5. Model diagnostics. 3.6. Extensions. 4. Regularization methods. 4.1. Introduction. 4.2. Ridge regression. 4.3. LASSO regression. 4.4. Elastic Net. 4.5. Selection of tuning parameters. 5. Dimension reduction methods. 5.1. Introduction. 5.2. Principal component regression. 5.3. Partial least squares. 6. Ensemble methods. 6.1. Introduction. 6.2. Boosting. 6.3. Bagging. 6.4. Stacking.

Lectures: the contents of the course will be introduced, explained and illustrated with examples. Teaching materials will be provided on Aula Global. Computer Labs: Examples and cases studies with the R language.