The Syllabus of the Master in Artificial Intelligence is made up of eighteen courses organized in three training areas, granting a total of 110 credits, which are taken over four semesters (2 years).
The courses that make up the study plan are of a theoretical-practical nature, taking into account the postgraduate research modality. In addition, each subject as a tool for flexibility, must contain activities such as consulting through the use of ICT tools, attending conferences and videoconferences, as well as the development of collaborative work. Below is the List of courses, with their credit value and the approach to theoretical hours and practical hours.
Disciplinary Area : It is made up of nine courses which are taught in the first two semesters and provide the student with the knowledge, skills and attitudes necessary for basic training in Artificial Intelligence.
Research Area : It is made up of:
- Two Research Seminar courses, one in the third semester and the other in the fourth (this is where each student’s research project is developed).
- Two practical courses on research methodology and thesis protocol design.
- A course called «research experience» in the fourth semester, which includes activities such as research stays, publication of results in specialized forums and / or dissemination of knowledge.
Optional training area: it is made up of various courses framed in the postgraduate LGAC’s. Of these, the student will take two in the second semester and two in the third. The choice of elective courses will be according to the research project of each student.
Elective Courses of the Master in Artificial Intelligence:
- Learning, simulation and agents.
- High Performance Computing.
- Evolutionary Computation.
- Computational Geometry.
- Engineering Optimization.
- Multi-Objective Evolutionary Optimization.
- Bayesian Networks.
- Neural Networks.
- Mobile Robotics
- Multi-Agent Systems.
- Computer Vision.
- Semantic Web.
- Select Artificial Intelligence Topics
- Selected Learning Topics
- Select Robotics Topics
- Selected Data Mining Topics
- Selected Optimization Topics
CURRICULAR MAP
| SEMESTER | COURSE | No. hours | No. of Credits |
| First | Introduction to Artificial Intelligence | 4 | 7 |
| Programming for Artificial Intelligence | 4 | 7 | |
| Algorithm Analysis | 4 | 7 | |
| Automata and Formal Languages | 4 | 7 | |
| Second | Probabilistic Methods for Artificial Intelligence | 4 | 7 |
| Computability | 4 | 7 | |
| Knowledge Representation | 4 | 7 | |
| Machine learning | 4 | 7 | |
| Elective I | 4 | 7 | |
| Elective II | 4 | 7 | |
| Third | Elective III | 4 | 7 |
| Elective IV | 4 | 7 | |
| Research Seminar I | 4 | 4 | |
| Fourth | Research Seminar II | 4 | 4 |
| Research Experience | 4 | 7 | |
