Become familiar with different analytical tools, based on data, to make business decisions Capacity to develop skills to analyze and find relationships between many variables/features Know how to evaluate supervised-learning models Develop skills to classify observations based on probabilistic learning and machine learning tools Handle the R language for statistical-learning tools

Introduction to Statistical Learning Performance Evaluation of Learning Models Bayesian Learning Bayes Rule and Cost-Sensitive Learning k-NN Support Vector Machines Decision Trees and Random Forests Neural Networks

Lectures (50% of the sessions): the contents of the course will be introduced, explained and illustrated with examples. Teaching materials will be provided on Aula Global. Computer Labs (50% of the sessions): Examples and cases studies with the R language.