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. Linear Regression 1.1. Mean Linear Regression: A Brief Review 1.2. Quantile Linear Regression 1.2.1. Quantile Regression Model 1.2.2. Model Estimation 1.2.3. Inference on Parameters 2. Nonlinear Relationships 2.1. Transformations, Interactions, and Polynomial Regression 2.2. Nonlinear Regression Models 2.2.1. Model Estimation 2.2.2. Inference on Parameters 3. Regularization Methods 3.1. Bias-Variance Trade-off 3.2. Ridge Regression. LASSO Regression. Elastic Net 3.3. Adaptive LASSO. Symmetric and Non-Concave Penalties. Oracle Property 3.4. Tuning Parameter Selection 4. Generalized Regression Models 4.1. Generalized Linear Model: A Brief Review 4.2. Model Selection and Diagnosis 4.3. Generalized Additive Models 4.3.1. Smoothing Methods: B-splines. Penalized Splines 4.3.2. Estimation. Tuning Parameter Selection 5. Ensemble Methods 5.1. Introduction 5.2. Boosting 5.3. Bagging 5.4. 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.