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. RA2.2: Ability to identify, formulate and solve engineering problems in their field of study; to select and apply relevant methods from established analytical, computational and experimental methods; to recognise the importance of non-technical societal, health and safety, environmental, economic and industrial constraints. RA3.1: Ability to develop and design complex products (devices, artefacts, etc.), processes and systems in their field of study to meet established requirements, that can include an awareness of non-technical ¿ societal, health and safety, environmental, economic and industrial ¿ considerations; to select and apply relevant design methodologies. RA4.1: Ability to conduct searches of literature, to consult and to critically use scientific databases and other appropriate sources of information, to carry out simulation and analysis in order to pursue detailed investigations and research of technical issues in their field of study. RA5.1: Understanding of applicable techniques and methods of analysis, design and investigation and of their limitations in their field of study.

1. Introduction to Data Analysis and Data Mining 2. Machine learning with numeric techniques 3. Numerical learning 4. Evaluation of Machine Learning Models 5. Attribute analysis 6. Methodology of data mining projects 7. Introduction to other advanced techniques (combination, SVM , Fuzzy systems, GAs)

Seminars and lectures supported by computer and audiovisual aids. Practical learning based on cases and problems, and exercise resolution. Individual and group or cooperative work with the option of oral or written presentation. Individual and group tutorials to resolve doubts and queries about the subject. Internships and directed laboratory activities.