Basic knowledge of statistics and programming

This course covers the main fundamentals of machine learning, from a very practical approach we are going to work for making a computer to be able to build models that allows it to learn concepts or recognize patterns, and to be able to define them and/or predict new incoming instances, and all this without being programmed explicitly.

1. Introduction to machine learning and inductive learning 2. Supervised Learning I: Trees and Decision Rules 3. Evaluation and validation of learning models 4. Machine learning methodology 5. Supervised Learning II: Regression Trees, Instance-Based Learning, and Ensembles of Classifiers 6. Unsupervised and semi-supervised learning techniques 7. Relational machine learning

Formation activities AF1 - Theoretical class AF2 - Practical classes AF3 - Theoretical and practical classes AF5 - Tutorials AF6 - Group work AF7 - Individual student work AF8 - Partial and final exams -> Presentations and/or partial and final dissertations teaching methodology MD1 - Presentations in the teacher's class with the support of computer and audiovisual media, in which the main concepts of the subject are developed and the bibliography is provided to complement the learning of the students. MD2 Critical reading of texts recommended by the professor of the subject: articles, reports, videos, tutorials, etc., either for later discussion in class, or to broaden and consolidate knowledge of the subject. MD3 Resolution of practical cases, problems, etc. raised by the teacher individually or in groups MD5 Preparation of work and reports individually or in groups