- Artificial Intelligence (Course 2 / Semester 2) - Machine Learning (Course 3 / Semester 2)

The objective of the course is to provide the student with the necessary knowledge about the applications of Artificial Intelligence, through its various paradigms, in the solution of problems in a wide range of sectors. In the same way, the student should know the principles, methods, and techniques of Artificial Intelligence.

RA1.1: Knowledge and understanding of the mathematics and other basic sciences underlying their engineering specialisation, at a level necessary to achieve the other programme outcomes. 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. RA7.1: Ability to communicate effectively information, ideas, problems and solutions with engineering community and society at large. RA7.2: Ability to function effectively in a national and international context, as an individual and as a member of a team and to cooperate effectively with engineers and non-engineers. 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. 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. CTE3: Ability to acquire, obtain, formalise and represent human knowledge in a computable form for the resolution of problems by means of a computer system in any field of application, particularly those related to aspects of computation, perception and performance in intelligent environments. 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 - Context. - Key Features. - Main techniques. 2. Expert Systems - Introduction. - What is an Expert System? - Applications of Expert Systems. - Advantages and Disadvantages - Case studies 3. Neural Networks - Introduction - Artificial Neural Networks - Applications of ANN - Advantages-Disadvantages - Case studies 4. Evolutionary Algorithms - Introduction - Evolutionary algorithms - Applications of evolutionary algorithms - Advantages and disadvantages - Case studies 5. Data Mining - Introduction - Applications - KDD and Data Mining - Data Mining Tasks - Applications - Case studies 6. Text mining - Definition - Overall Architecture of Text Mining Systems - Core Text Mining Operations - Applications - Case studies 7. Web Mining - Introduction - Types of Web Mining - Content Web Mining - Structure Web Mining - Usage Web Mining - Case studies 8. Fuzzy Logic - The concept of fuzzy - Fuzzy Sets - Fuzzy logic - Fuzzy reasoning systems - Case studies 9. Agents - Introduction - What is an agent? - Multiagent Systems - Applications - Case studies 10. Other techniques - Introduction - Description - Applications - Case studies

- Lectures (0,6 ECTS). They are aimed at achieving the specific cognitive competencies of the course. The fundamental ideas of the subject will be presented. - Seminars (0,3 ECTS). They have the purpose of complementing the acquisition of specific cognitive competencies. In addition, they develop some transversal competencies such as the capacity of analysis and synthesis and teamwork. - Practical classes (0,8 ECTS). They develop the specific instrumental competencies and most transversal competencies, such as teamwork, the ability to apply knowledge to practice, planning and organizing, and analyzing and synthesizing. They also aim to develop specific attitudinal skills. - Directed Academic Activities (4.3 ECTS) -- With the presence of the professor. Participation in classes oriented by the professor where aspects of the subject are deepened and evaluated. Evaluation tests of theoretical and practical knowledge. -- Without the presence of the professor. Exercises, complementary readings proposed by the professor, preparation of classes, practice. Participation in the SPOC of the course. - Tutorials: Individualized assistance (individual tutorials) or in group (collective tutorials) to the students by the professor.