Integrated project of AI I

The aim of this course is for students to work as a team to find solutions to practical problems through an AI project. In addition to requiring the integration of knowledge acquired during the degree, the course will promote the development of interpersonal, communication and teamwork skills. Students are expected to be proactive in the search for methods and techniques appropriate to the problem to be addressed in their project.

The course is structured in two clearly differentiated blocks.
- Training and practice in professional and communicative skills, which students will have to know and apply in the presentation of the work carried out in the project (both in their presentation and in the descriptive reports).
- Carrying out the project itself, to which most of the time will be devoted through tutoring.

As a whole, the list of contents is as follows:
1. Introduction to AI-based projects
2. Problem definition and scope
3. Interpersonal skills and teamwork
4. Effective presentations
5. Introduction to scientific and technical reports
6. Bibliography management and report preparation
7. AI project development.

Given that the Integrating Project I is a subject that essentially tries to integrate the learning acquired during the first two years of the degree, and follows a teaching methodology of learning based on challenges and projects, there is no specific bibliography.
The references, resources and materials necessary for the development of the subject will be provided by the teacher in charge in the presentation and in the introductions to the different topics and objectives of the subject. In spite of this, several (non exhaustive) support references which will be used in the course follow:

AI project management and legal framework

- Andrew Ng, How to Build Your Career in AI. A Simple guide. https://info.deeplearning.ai/how-to-build-a-career-in-ai-book.
- ISO/IEC 42001:2023 Information technology — Artificial intelligence — Management System https://www.iso.org/standard/81230.html, 2023
- AI Act https://data.consilium.europa.eu/doc/document/PE-24-2024-INIT/es/pdf, 2024.

Teamwork and effective communication

- Jonathan Shewchuk, Three Sins of Authors in Computer Science and Math, University of Berkeley, 1997. https://www.cs.cmu.edu/~jrs/sins.html
- Michael Ernst, How to write a technical paper or a research paper, https://homes.cs.washington.edu/~mernst/advice/write-technical-paper.ht…, 2023
- Carmine Gallo, What It Takes to Give a Great Presentation, https://hbr.org/2020/01/what-it-takes-to-give-a-great-presentation, 2020.
- Mariona Casas Deseuras, ¿Saber hablar en público es una habilidad innata?, https://theconversation.com/saber-hablar-en-publico-es-una-habilidad-in…, 2021
- Chris Anderson, How to Give a Killer Presentation. Lessons from TED, https://hbr.org/2013/06/how-to-give-a-killer-presentation, 2013.

AI general Models and strategies generales, which will be complemented depending on the challenge or project developed by each group.
- S. Russell, P. Norvig. Artificial Intelligence. A Modern Approach. 4th ed. Pearson, 2022.
- R. Marín, J.T. Palma (Eds.) Inteligencia Artificial y Sistemas Inteligentes. Ed. McGraw-Hill, 2008.
- Curso de Inteligencia Artificial. Fernando Sancho Caparrini. http://www.cs.us.es/~fsancho

Competences

SPECIFIC
- CE12] Knowing the fundamentals of artificial intelligence algorithms and models for solving problems of a certain complexity, understanding their computational complexity and having the ability to design new models.
- SC15] Knowing and knowing how to apply and correctly explain the validation techniques of artificial intelligence solutions.
TRANSVERSALS
- TR1] Ability to communicate and transmit knowledge, skills and abilities.
- TR2] Ability to work in a team, in interdisciplinary environments and managing conflicts.
- TR3] Ability to create new models and solutions autonomously and creatively, adapting to new situations. Initiative and entrepreneurial spirit.
- TR4] Ability to introduce the gender perspective in models, techniques and solutions based on artificial intelligence.
- TR5] Ability to develop models, techniques and solutions based on artificial intelligence that are ethical, non-discriminatory and reliable.
- TR6] Ability to integrate legal, social, environmental and economic aspects inherent to artificial intelligence, analysing its impacts, and committing to the search for solutions compatible with sustainable development.



Learning outcomes

- Be able to identify and know the basic stages necessary to successfully address an AI project.
- Design, develop and evaluate an IA project.
- Write a scientific-technical report of the project carried out.
- Present in public (to teachers and peers) the work carried out, demonstrating and critically communicating the main results achieved with the development of the project.

The teaching methodology will be based on individual and group work through learning based on exercises, case studies, challenges and projects, so as to encourage autonomous and proactive learning, based on objectives.
The lectures will present the keys to be taken into account in order to develop effective presentation and communication skills for specialised and non-specialised audiences, teamwork, as well as the preparation of documentation and scientific-technical reports. The presentations will be accompanied by exercises to develop these skills in a practical way.
In the interactive sessions, the exercises will be carried out and the results will be presented and discussed. The challenges to be solved in the integrative project will also be presented, in which the groups will have to apply the knowledge and skills acquired so far in the previous courses, and will develop new learning based on the realisation of the project, as well as its presentation, discussion and defence.
Dynamics and exercises will be developed to promote teamwork, effective communication to specialised and non-specialised audiences.
The teaching will be supported by the USC virtual platform in the following way: repository of documentation related to the subject (texts, presentations, recommended readings...) and virtual tutoring of students (e-mail, forums).

The learning assessment considers the evaluation of all the practical activities proposed by the teachers:
- Training in professional skills (30%): classroom presentations and delivery of exercises or assignments.
- Completion of the project (70%): preparation of the report, documentation and defence of the project.

The course may include carrying out or participating in mandatory activities presented by the teacher (eg, attending or participating in talks, seminars, workshops or visits) which will be a part of the previously indicated assessment components (depending on the type of activity). Such participation will be presented either in the course presentation or during the semester.

The minimum grade in each of the parts must be equal to or higher than 4 out of a maximum of 10 points in order to pass the subject as a whole.
Once the above requirement has been met, the final grade for the subject will be the arithmetic mean weighted by the percentages indicated above for the two parts. In the event of incurring in any of the situations indicated above due to not achieving the minimum mark necessary to pass the subject as a whole in one or more parts, the final mark for the opportunity will be the minimum of the marks obtained in those parts.

Students who have not completed the delivery of any other compulsory activity will be given the grade of ‘not presented’.

In order to pass the subject at the second opportunity, students must undergo the assessment of all those compulsory parts pending, in accordance with the above. For the rest, the grades obtained during the course will be retained.

In the case of fraudulent performance of exercises or tests, the provisions of the regulations on the evaluation of students' academic performance and revision of grades will apply (https://www.xunta.gal/dog/Publicados/2011/20110721/AnuncioG2018-190711-…). In application of the ETSE regulations on plagiarism (approved by the Xunta da ETSE on 19/12/2019), the total or partial copying of any practical or theory exercise will result in the failure of the two opportunities of the course, with a grade of 0.0 in both cases (https://www.usc.es/etse/files/u1/NormativaPlagioETSE2019.pdf).

Classroom work time: 51 total hours, divided into 10h (lectures), 40h (seminars and practicals), 1h (tutorials).
Personal work time: 99h (total), divided into 9h (self-study of theory and practicals) and 90h (completion, documentation, presentation and defence of the project).

It is recommended that students continuously carry out the exercises proposed in class, and make use of the tutorials to resolve any doubts they may have.

It is recommended to have passed all the subjects of the first two years of the Degree.
The course will be taught in Spanish and Galician, but part of the contents, bibliography or other references may be in English.