After this course students will understand the principles underlying the general regression, classification and data analysis problems, and will become familiarized with the different approaches for dealing with them. Students will learn that, for the correct understanding of these problems, it is necessary to master three basic probability theory elements: 1) the likelihood, 2) the difference between a priori and a posteriori uncertainty, and 3) Bayes' Theorem.
From a practical point of view, students will be presented different approaches for learning from data to solve these problems: non-parametric techniques, methods based on empirical risk minimization, or those that follow Bayesian principles.
More specifically, the following list summarizes the main objectives of this course, enumerated as competences to be acquired by the students:
- knowledge of the theoretic principles underlying several of the most important techniques for learning from data.
- ability to apply such techniques on real problems, and to extract results and conclusions.
- understanding of classic methods for estimation and classifications, and skills for their correct application.
- ability to use machine learning tools: gaussian processes, support vector machines, non-parametric methods
- knowledge of other data analysis problems, like topic modeling or recommendations systems