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

K10: Knowledge and application of the necessary tools for storing, processing and accessing information systems, including web_based systems. S2: Ability to understand the fundamentals, paradigms and techniques of intelligent systems and to analyze, design and build systems, services and computer applications that use these techniques in any field of application. S5: Ability to know and develop computational learning techniques and 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. S13: Ability to plan, conceive, deploy and manage IT projects, services and systems in all areas, leading their implementation and continuous improvement and assessing their economic and social impact. S14: Ability to understand the importance of negotiation, effective work habits, leadership and communication skills in all software development environments. C2: To Know and be able to handle interpersonal skills on initiative, responsibility, conflict resolution, negotiation, etc., required in the professional environment. C7: Ability to understand and implement intelligent systems paradigms and techniques in the analysis, design and development of advanced computing solutions, applicable to various areas such as automation, machine learning and decision making. C9: Ability to identify and formulate software solutions based on current models and techniques, developing, verifying, validating and documenting software in accordance with quality standards and good practices in software engineering.

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.