Programming (Course: 1 / Semester: 1) Statistics (Course 2 / Semester: 1) Automata and Formal Language Theory (Course 2 / Semester 1) Artificial Intelligence (Course 2 / Semester 2)

* Understand the basic techniques of Machine Learning * Learn to determine when to use Machine Learning in real problems * Learn to determine which technique is appropriate for each problem * Learn to apply the techniques in real problems from a practical point of view

RA1.2: Knowledge and understanding of engineering disciplines underlying their specialisation, at a level necessary to achieve the other programme outcomes, including some awareness at their Forefront. RA5.1: Understanding of applicable techniques and methods of analysis, design and investigation and of their limitations in their field of study. RA6.2: Ability to manage complex technical or professional activities or projects in their field of study, taking responsibility for decision making. CG2: Be able to generate new ideas (creativity), to anticipate new situations, to adapt to new situations, working in a team and interact with others, but at the same time be able to work autonomously. CG7: Be able to present and discuss proposals in a team work environment, demonstrating personal and social skills that allow him/her to assume different responsibilities within them. CGO9: Ability to solve problems with initiative, decision-making, autonomy and creativity. Ability to know how to communicate and convey the knowledge, skills and abilities of the profession of Technical Engineer in Computer Science. CTE2: Ability to know the fundamentals, paradigms and techniques of intelligent systems and to analyse, design and build systems, services and computer applications that use these techniques in any field of application. CTE5: Ability to know and develop computational learning techniques, and also to design and implement applications and systems that use them, including those dedicated to the automatic extraction of information and knowledge from large volumes of data.

1. Introduction to Machine Learning 2. Basic classification and regression techniques 3. Advanced classification and regression techniques 3. Methodology (model evaluation, hyper-parameter tuning, preprocessing) 4. Unsupervised techniques (clustering, associative learning) 5. Reinforcement learning

* Lectures: 1 ECTS. Oriented, among others, towards the competences related to the fundamentals, paradigms and techniques useful to build and evaluate intelligent systems based on Machine Learning. * Practical/Lab sessions: 1 ECTS. Oriented towars the specific instrumental competences and competences about problem solving and application of acquired knowledge. * Continuous assessment tests (individual work): 0,5 ECTS. Oriented towards the competences related to the fundamentals, paradigms and techniques useful to build and evaluate intelligent systems based on Machine Learning. * Practical works (team work): 3 ECTS. Oriented to develop and integrate the specific competences related to the resolution and implementation of practical cases, generating a report including the problem definition, the technique applied, the obtained results and their interpretation. * Tutorials: Individualized or collective tutorials with the teacher. * Final exam: 0,5 ECTS. Its objective is to influence and complement the development of specific cognitive abilities, especially the analysis, design, representation and formalization of knowledge and the application of techniques for solving problems.