Computational Aspects of Cognitive Science

- To know the computational models of the human mind
- To distinguish the basic processes associated with human intelligence
- To know the main computational approaches to social cognition

1. Computational models of the human mind.
2. Elements of a cognitive architecture and types of architectures.
3. Types of memories and their use.
4. Forms of knowledge representation.
5. Types of learning

Basic:

Kahneman, D. (2012). Pensar rápido, pensar despacio. Debate.

1. Basic and general

CG1 - Maintain and extend well-founded theoretical approaches to allow the introduction and exploitation of new and advanced technologies in the field of Artificial Intelligence.
CG2 - Successfully address all stages of an Artificial Intelligence project.
CG3 - Search and select the useful information necessary to solve complex problems, handling with ease the bibliographical sources of the field.
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
CB9 - That students know how to communicate their conclusions and the ultimate knowledge and reasons that support them to specialized and non-specialized audiences in a clear and unambiguous way

2. Transversals

CT3 - Use the basic tools of information and communication technologies (ICT) necessary for the exercise
of their profession and for lifelong learning.
CT5 - Understand the importance of entrepreneurial culture and know the means available to entrepreneurs.
CT7 - Develop the ability to work in interdisciplinary or transdisciplinary teams, to offer proposals that contribute to sustainable environmental, economic, political and social development.
CT8 - Assess the importance of research, innovation and technological development in the socioeconomic and cultural advancement of society.

3. Specific

CE5 - Ability to design and develop intelligent systems by applying inference algorithms, knowledge representation and automatic planning.
CE6 - Ability to recognize those problems that require a distributed architecture that is not preset during the system design, which will be suitable for the implementation of intelligent multi-agent systems.
CE7 - Ability to understand the implications of developing an explainable and interpretable intelligent system.
CE8 - Ability to design and develop secure intelligent systems, in terms of integrity, confidentiality and robustness.

Project-based learning: students are given practical projects whose scope requires that an important part of the student's total dedication be dedicated to the subject. In addition, due to the scope of the work to be carried out, it is required not only that the students apply management skills as well as skills of a technical nature.

Tutorials: the teaching staff will assist the students in individualized tutorial sessions dedicated to orientation in the study and the resolution of doubts about the contents and work of the subject.

Autonomous work: the teaching staff presents the students with a project whose scope and objectives require that the students work on it autonomously, although under the supervision of the subject's teaching staff. In general, it is applied to work with a temporal scope and effort greater than that of laboratory practices.

Case study: students are presented with a work scenario, real or fictitious, that presents a specific problem. Students must apply the theoretical and practical knowledge of the subject to find a solution to the question or questions raised. As a general rule, the case study will be carried out in groups. The different working groups will present and share their solutions.

Expository method / master class: the teacher presents a topic to the students with the aim of providing a set of information with a specific scope. This teaching methodology will be applied to the training activity "Theory classes".

Laboratory practices: the teaching staff of the subject poses to the students a problem or problems of a practical nature whose resolution requires the understanding and application of the theoretical-practical contents included in the contents of the subject. The students can work on the solution to the problems posed. individually or in groups. This teaching methodology will be applied to the training activity "Laboratory practical classes" and may be applied to the training activity "Problem-based learning sessions, seminars, case studies and projects".

The evaluation of learning considers both the theoretical and the practical part. To pass the subject, an overall grade equal to or greater than 5 must be achieved, out of a maximum score of 10 points in the planned assessment activities. The weight of each of the parts is as follows:

E1: Final exam: 50%
E2: Evaluation of practical work: 50%

Students who have not taken the exam or have not submitted to the evaluation of any other compulsory activity will obtain the qualification of not presented.
To pass the subject on the second opportunity, students must submit to the evaluation of all those parts or pending mandatory deliveries that are established. For the rest, the qualifications obtained during the course will be kept.
In the case of fraudulent performance of exercises or tests, the provisions of the regulations for evaluating the academic performance of students and reviewing grades will apply (https://www.xunta.gal/dog/Publicados/2011/20110721 /AnnouncementG2018-190711-4180_gl.html). In application of the ETSE regulations on plagiarism (approved by the Xunta da ETSE on 12/19/2019), the total or partial copy of any practice or theory exercise will mean failing the two opportunities of the course, with the grade of 0.0 in both cases (https://www.usc.es/etse/files/u1/NormativaPlagioETSE2019.pdf).

Face-to-face work time: 21 total hours. The face-to-face sessions are divided into: 10h (Theory classes), 7h (Laboratory practical classes), 4h (Problem-based learning, seminars, case studies and projects).

Personal work time: 54h (total)

It is recommended that the students solve, implement, verify and validate all the proposed exercises and practices (not only the evaluable ones). It is also considered important to make use of tutorials to resolve doubts and active participation in expository and interactive sessions.

The subject is taught in English.