After this course students will understand the principles of estimation, decision, and grouping problems, and will become familiarized with the different approaches for dealing with them. Students will understand 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 followint 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 clasic methods for estimation and clasifications, and skills for their correct application.
- ability to use machine learning tools: neural networks, support vector machines, etc.
- knowledge of last generation approaches, including those based on machine ensembles