Probability I Probability II Linear Algebra Statistical inference methods I Stochastic processes Statistical inference methods II Multivariate Analysis

COMPETENCES 1. Capacity for identifying problems associated with statistical data in several variables. 2. Basic knowledge for handling vectors and matrices. 3. Acquire skills in multivariate data description. 4. Capacity for making and interpreting plots of multivariate datasets. 5. Know the properties of multivariate distributions. 6. Capacity for making hypotesis testing on a multivariate population. 7. Acquire skills in principal component analysis. 8. Acquire skills in heterogeneity problems such as outlier detection, hypothesis testing for different means and classification. 9. Handle statistical software for multivariate analysis. SKILLS 1. Aptitude to understand a real problem and to analyze it as an statistical problem. 2. Modeling and solving problems. 3. Capacity of analysis and synthesis. 4. Oral and written skills. 5. Aptitude to work in a group.

1. Introduction to statistical learning. 2. Linear regression. 3. Linear classification. 4. Resampling methods. 5. Selection and regularization of linear models. 6. Nonlinear methods. 7. Tree-based methods. 8. Support vector machines. 9. Unsupervised learning.

Theory (4 ECTS): Theoretical classes with support material taken from the web. Practical classes (2 ECTS): Problem solving classes. Computing classes in computer halls. Work assignments in groups. Oral presentations and debates. Tutorial classes before the midterm exam. Tutorial classes during the week 15.