Degree | Type | Year |
---|---|---|
Artificial Intelligence | FB | 1 |
You can view this information at the end of this document.
No prerequisites are required.
The goal of this course is to provide a multidisciplinary approach to AI research by integrating the latest advances in cognitive neuroscience. Today, the human brain is the most advanced and efficient information processor known, making it a crucial biological model for current and future AI systems. Students will explore cutting-edge theories on how the brain processes information and performs various cognitive functions, such as learning, memory, perception, language, decision-making, and emotion. The course will also examine how these functions are currently implemented in AI models, alongside a detailed description of the corresponding human cognitive processes.
1. An introduction to cognitive psychology (3 sessions)
2. Attention (1 session)
3. Perception (2 sessions)
4. Learning (1 session)
5. Memory (2 sessions)
6. Decision-making (1 session)
7. Emotion & Motivation (1 session)
8. Language & Consciousness (1 session)
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Master classes | 24 | 0.96 | 1, 12, 6, 5, 8, 4, 7, 9 |
Seminars | 24 | 0.96 | 1, 3, 6, 10 |
Type: Supervised | |||
Tutoring (group and individual) | 20 | 0.8 | 10, 13 |
Type: Autonomous | |||
Individual Study | 50 | 2 | 1, 2, 12, 6, 5, 8, 4, 7, 9 |
Team work | 20 | 0.8 | 3, 10, 13 |
The teaching methodology is based on various formative activities. Over the 12.5 weeks of the course, lectures, seminars, workshops, supervised activities, and independent activities will be scheduled.
Within the schedule established by the center or the curriculum, 15 minutes of one class will be reserved for students to evaluate the teaching staff and the subjects or modules through questionnaires.
For this subject, the use of Artificial Intelligence (AI) technologies is permitted exclusively for support tasks, such as bibliographic or information searches, text correction, or translations.
The student must clearly identify which parts were generated using this technology, specify the tools used, and include a critical reflection on how these tools influenced the process and the final result of the activity.
Lack of transparency in the use of AI in an assessable activity will be considered academic dishonesty and may result in partial or total penalties in the activity's grade, or more severe sanctions in serious cases.
All teaching materials provided by the teaching staff, as well as students’ written work and oral presentations, must avoid the use of sexist language.
Inclusive and non-discriminatory language must be used consistently to promote gender equality and respect for all identities.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
First Partial Exam | 25% | 2 | 0.08 | 1, 12, 4, 7, 9, 11, 10, 13 |
Laboratory practices (PLAB) | 30% | 4 | 0.16 | 1, 2, 3, 9, 11, 13 |
Second Partial Exam | 25% | 2 | 0.08 | 1, 2, 6, 5, 8, 7, 9, 11 |
Seminars (PAUL) | 20% | 4 | 0.16 | 3, 6, 5, 8, 4, 7, 10, 13 |
The evaluation of this subject is conducted continuously and has a clear formative purpose. The competencies associated with this subject will be assessed through follow-up activities, group presentations and reports, as well as exams. The learning evidence that students must submit will reflect the content and competencies addressed in theoretical classes, seminars, and laboratory practicals.
The evaluation system is structured around five types of evidence, each contributing a specific weight to the final grade:
Evidence 1 (Master Class Evaluation): Exams
Evidence 2 (Seminar Evaluation):
Evidence 3 (Laboratory Session Evaluation):
To pass the subject, students must meet the following conditions:
Failure to meet the third criterion (i.e., submitting fewer than 4 evidences) will result in the subject being marked as Not Assessable.
Attendance is mandatory for all PLAB and PAUL sessions. Any absences must be justified with an official document.
To be eligible for a retest, students must:
The retest will consist of a comprehensive exam covering all theoretical content. To pass the subject, students must score 5 or more points on the retest. Additionally, the average score across all remaining evidences must still be greater than 5. The maximum grade achievable through the retest is Approved (5.0).
This subject does not offer a synthesis test for second or subsequent enrollments.
This subject does not offer the option of a single assessment (i.e., a single final evaluation).
In cases of copying, plagiarism, or similar misconduct, the grade for the affected activity will be zero (0), without prejudice to any additional disciplinary actions deemed appropriate.
Eysenck, M.W. & Keane, M.T. (2020). Cognitive Psychology. A Student’s Handbook. Routledge.
Eysenk, M.W. & Groome, D. (2015). Cognitive Psychology: Revisiting the classic studies.
Gazzaniga, M. S., & Mangun, G. R. (Eds.). The cognitive neurosciences (5th ed.). Boston Review.
Churchland, P. S., & Sejnowski, T. J. (1992). The computational brain. The MIT Press.
Goldstein, E. B. Sensation and perception (8th ed.). Wadsworth.
Bringing a personal laptop may be required for some lectures. Specific dates will be communicated by the lecturer.
Please note that this information is provisional until 30 November 2025. You can check it through this link. To consult the language you will need to enter the CODE of the subject.
Name | Group | Language | Semester | Turn |
---|---|---|---|---|
(PAUL) Classroom practices | 711 | English | first semester | morning-mixed |
(TE) Theory | 71 | English | first semester | afternoon |