Competencies and objectives

 

Course context for academic year 2025-26

The Master's Thesis (TFM) consists of carrying out a project in the field of artificial intelligence, in which all the competences and skills acquired in the different subjects taken in the Master's Degree in Artificial Intelligence are integrated. The work will be individual and under the guidance of one or more tutors who will have the task of guiding and advising the student in each of the phases of the development of the project. The TFM will be presented and defended before a university tribunal, by means of a public exhibition.

The TFM has two main objectives. On the one hand, the student has the opportunity to study a subject of interest to him/her in depth. On the other hand, it allows them to develop fundamental skills and abilities, such as the ability to plan a process, solve problems, analyse and interpret results, or defend proposals through efficient communication, among others.

 

 

Course competencies (verified by ANECA in official undergraduate and Master’s degrees) for academic year 2025-26

Transversal Competences

  • CT1 : Be able to lead projects related to artificial intelligence, as well as to manage work teams.
  • CT2 : Demonstrate computer and information skills in the field of artificial intelligence.
  • CT3 : Show oral and written communication skills.

 

General Competences

  • CG1 : Apply the knowledge acquired to real problems related to artificial intelligence.
  • CG10 : Be able to use engineering principles and modern computer technologies to research, design and implement new applications of artificial intelligence,
  • CG2 : Be able to develop and learn in a self-directed or autonomous way topics related to artificial intelligence.
  • CG3 : Know how to operate in multidisciplinary and/or international contexts, providing solutions from the point of view of artificial intelligence.
  • CG4 : Know and apply in each situation the social, ethical and legal responsibilities linked to artificial intelligence.
  • CG5 : Know how to manage the available information and resources related to artificial intelligence.
  • CG6 : Being able to adapt to environments related to artificial intelligence, fostering teamwork, creativity,
  • CG7 : To be able to adapt to the constant evolution of the discipline and to understand and apply new technical and scientific developments related to artificial intelligence.
  • CG8 : Knowing how to plan, design, develop, implement and maintain products, applications and services related to artificial intelligence, taking into account technical, economic and efficiency aspects.
  • CG9 : Know how to manage projects related to artificial intelligence, complying with current regulations and ensuring the quality of the service.

 

Specific Competences

  • CE22 : Design, develop, present and defend, individually in front of a university examining board, a comprehensive work on artificial intelligence that synthesises the knowledge acquired in the teachings.

 

Basic Competences

  • CB10 : That students possess the learning skills that allow them to continue studying in a way that will be largely self-directed or autonomous.
  • CB6 : Possess and understand knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context
  • CB7 : That students know how to apply the knowledge acquired and their ability to solve problems in new or little-known environments within broader (or multidisciplinary) contexts related to their area of ¿¿study
  • CB8 : Students are able to integrate knowledge and deal with the complexity of making judgements on the basis of incomplete or limited information, including reflections on the social and ethical responsibilities associated with information which, while incomplete or limited, includes reflections on the social and ethical responsibilities linked to the application of their knowledge and judgements.
  • CB9 : Students are able to communicate their conclusions and the ultimate knowledge and rationale behind them to specialist and non-specialist audiences in a clear and unambiguous way.

 

 

 

Learning outcomes (Training objectives)

- Design, develop, present and defend, individually before a university examining board, a comprehensive work related to artificial intelligence in which the knowledge acquired in the courses is synthesised.

 

 

Specific objectives stated by the academic staff for academic year 2025-26

To design, develop, present and defend, individually before a university examining board, a comprehensive work related to artificial intelligence in which the knowledge acquired in the courses is synthesised.

 

 

General

Code: 43511
Lecturer responsible:
Escalona Moncholí, Félix
Credits ECTS: 9,00
Theoretical credits: 0,00
Practical credits: 0,90
Distance-base hours: 8,10

Departments involved

  • Dept: Computer Science and Technology
    Area: Architecture and Technology of Computers
    Theoretical credits: 0
    Practical credits: 0,22
  • Dept: Physics, Systems Engineering and Sign Theory
    Area: Systems Engineering and Automatics
    Theoretical credits: 0
    Practical credits: 0,22
  • Dept: Software and Computing Systems
    Area: LANGUAGES AND COMPUTING SYSTEMS
    Theoretical credits: 0
    Practical credits: 0,22
  • Dept: Computer Science and Artificial Intelligence
    Area: Science of the Computation, Artificial Intelligence
    Theoretical credits: 0
    Practical credits: 0,24
    This Dept. is responsible for the course.
    This Dept. is responsible for the final mark record.

Study programmes where this course is taught