Linear algebra Probability and Data Analysis Introduction to Statistical Modeling

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. Introduction 1.1 Basics of multivariate data analysis and statistical learning 1.2 Supervised vs. unsupervised learning 1.3 Data visualization techniques 2. Supervised Learning: Regression 2.1 Linear regression 2.2 Linear model selection and regularization 2.3 Cross-validation on regression problems 3. Supervised learning 3.1 Logistic regression 3.2 Bayes classifier 3.3 Linear discriminant analysis and k-Nearest neighbor classifier 3.5 Random Forests 3.6 Support vector machines 4. Unsupervised Learning and Dimensionality Reduction Techniques 4.1 Clustering methods: k-means and hierarchical clustering 4.2 Principal component analysis 4.3 Multidimensional scaling 4.4 ISOMAP and locally-linear embedding

Theory (3 ECTS), Practice (3 ECTS). 50% lectures with teaching materials available on the Web. The other 50% practical sessions (computer labs).