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

Lectures Practice sessions Tutorship Team work Individual student work Presentations for partial and final assessments