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

1. Principal Component Analysis (PCA) 2. Multivariate Normal Distribution 3. Discriminant Analysis 4. Supervised Learning: k-Nearest Neighbors, Decision Trees, and Random Forests 5. Bias-Variance Tradeoff and Cross-Validation 6. Support Vector Machines (SVM) 7. Unsupervised Learning: K-means and Expectation-Maximization (EM) algorithm for Gaussian Mixture Models

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.