ECTS credits ECTS credits: 3
ECTS Hours Rules/Memories Hours of tutorials: 1 Expository Class: 10 Interactive Classroom: 11 Total: 22
Use languages English
Type: Ordinary subject Master’s Degree RD 1393/2007 - 822/2021
Departments: Electronics and Computing, External department linked to the degrees
Areas: Computer Science and Artificial Intelligence, Languages and Computer Systems, Área externa M.U en Intelixencia Artificial
Center Higher Technical Engineering School
Call: First Semester
Teaching: With teaching
Enrolment: Enrollable
Name of the subject: Emerging Topics and Entrepreneurship in AI
To understand the importance of innovation as a key success factor for creating value, growth, and sustainability.
To learn about the tools and experiences necessary for the creation of new companies (start-ups).
To understand the value of entrepreneurial culture and its impact on society.
To assess the economic and financial viability of a new business project.
To know financing opportunities
To know how to apply innovation models depending on market conditions.
Knowing and assessing the technological solutions based on Artificial Intelligence with the greatest innovative potential.
Basic aspects of innovation and entrepreneurship. Viability of a project. Business model and business plan. Agile project management methodologies. Funding. Innovation models and their application (intrapreneurship, Open Innovation, Closed Innovation...). Emerging topics (quantum artificial intelligence, AutoML...) and how they can impact and create new markets.
“Lean Startup para científicos: Una guía para científicos e investigadores visionarios que quieren transformar el mundo acercando sus innovaciones a la sociedad y el mercado”. José Javier Ruiz Cartagena and Carmen García Mora. Prismático. 2023.
Equivalent references in English and other content, also in English, will be provided on the virtual campus.
BASIC AND GENERAL
GC1 - Maintain and extend well-founded theoretical approaches to enable the introduction and exploitation of new and advanced technologies in the field of Artificial Intelligence.
GC2 - Successfully tackle all stages of an Artificial Intelligence project.
GC3 - Search for and select the useful information needed to solve complex problems, handling with fluency the bibliographic references in the field.
GC5 - Work in teams, especially multidisciplinary teams, and be skilled in the management of time, people and decision-making.
BC6 - Possess and understand knowledge that provides a basis or opportunity for originality in the development and/or application of ideas, often in a research context.
BC7 - Students are able to apply their acquired knowledge and problem-solving skills in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their field of study.
BC9 - Students are able to communicate their conclusions and the knowledge and rationale underpinning them to specialist and non-specialist audiences in a clear and unambiguous way.
BC10 - That students possess the learning skills that will enable them to continue studying in a way that will be largely self-directed or autonomous.
TRANSVERSALS
TC2 - To master the oral and written expression and comprehension of a foreign language.
TC3 - Use the basic tools of information and communication technologies (ICT) necessary for the exercise of their profession and for lifelong learning.
TC4 - Develop for the exercise of citizenship that respects democratic culture, human rights and the gender perspective.
TC5 - Understand the importance of entrepreneurial culture and know the means available to entrepreneurs.
TC7 - Develop the ability to work in interdisciplinary or transdisciplinary teams to offer proposals that contribute to sustainable environmental, economic, political and social development.
TC8 - To value the importance of research, innovation and technological development in the socio-economic and cultural progress of society.
TC9 - Have the ability to manage time and resources: develop plans, prioritise activities, identify critical ones, establish deadlines and meet them.
SPECIFIC
SC9 - Ability to have a deep knowledge of the fundamental principles and models of quantum computing and know how to apply them to interpret, select, evaluate, assess, model and create new concepts, theories, uses and technological developments related to Artificial Intelligence.
SC12 - Ability to understand, pose, formulate and solve problems that can be tackled through automatic learning models.
SC15 - Knowledge of computer tools in the field of automatic learning, and ability to select the most appropriate one for solving a problem.
SC27 - Understanding of the importance of entrepreneurial culture and knowledge of the means available to entrepreneurs.
SC30 - Being able to pose, model and solve problems that require the application of the Artificial Intelligence methods, techniques and technologies.
Learning activity
Problem-based learning, seminars, case studies and projects: these are sessions whose aim is for students to acquire certain competences based on the resolution of exercises, case studies and projects that require students to apply the knowledge and competences developed during the subject. These sessions may require students to present their solutions to the problems posed orally. The work carried out by students can be done individually or in working groups.
Theory classes: Oral presentation complemented by the use of audiovisual media and the introduction of some questions addressed to the students, with the aim of transmitting knowledge and facilitating learning. In addition to the oral presentation time by the lecturers, this training activity requires students to dedicate time to prepare and review the materials covered in the class on their own.
Practical laboratory classes: classes dedicated to students developing practical work that involves tackling the resolution of complex problems, and the analysis and design of solutions that constitute a means for their resolution. This activity may require students to present their work orally. The work carried out by students may be done individually or in working groups.
Teaching methodologies
Project-based learning: students are presented with practical projects whose scope requires them to dedicate a significant part of their total time to the subject. Furthermore, due to the scope of the work to be carried out, students are required to apply not only management skills but also technical skills. Tutorials: the teaching staff will attend to students in individualised tutorial sessions dedicated to guidance in the study and the resolution of doubts about the contents and work of the subject. Autonomous work: the teaching staff will set students a project whose scope and objectives require the students to work on it autonomously, although under the supervision of the subject's teaching staff. In general, it is applied to work with a greater scope in terms of time and effort than laboratory practicals. Case studies: students are presented with a work scenario, real or fictitious, which presents a specific problem. Students must apply the theoretical and practical knowledge of the subject to find a solution to the question or questions posed. As a general rule, the case studies will be carried out in groups. The different working groups will present and share their solutions. Expository method / master class: the teacher presents a subject 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 practicals: the teachers of the subject present students with 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. Students can work on the solution to the problems posed individually or in groups. This teaching methodology will be applied to the training activity "Practical laboratory classes" and may be applied to the training activity "Problem-based learning sessions, seminars, case studies and projects".
Attendance at both theory and practical classes is mandatory.
Final exam: exams will be held at the end of the course, especially aimed at evaluating the understanding of the knowledge presented in the theory classes. It will account for 20% of the final grade.
Assessment of practical work: the solutions proposed by the students to the practical exercises will be assessed. The assessment of practical work may be carried out by means of a correction by the teaching staff, a defence of the solution provided by the students before the teaching staff or an oral presentation of the solution developed. (Applicable to the results of the training activities "Practical laboratory classes", "Problem-based learning, seminars, case studies and projects" and "Carrying out tutored work"). It will account for 50% of the final grade.
Assessment of tutored work: the tutored work carried out by students will be assessed. The assessment of the tutored work will be carried out by means of a defence in which the students explain their proposal and conclusions to the teaching staff, or by means of an oral presentation of the solution in the classroom. It will account for 30% of the final grade.
The final grade will be the result of weighting the grades obtained in the final exam, the practical work and the tutored work, weighted in each case by the weights previously indicated for each concept. The subject will be considered passed if the final grade is equal to or higher than 5, not having had less than 4 in any of the three parts or concepts evaluated.
The grade will only be considered as not presented when a student does not take the final exam, nor is he/she graded for the total or partial completion of practical or tutored work. Otherwise, he/she will receive as a grade the one resulting from the evaluations obtained, even if they do not correspond to the whole of the subject or the concepts evaluated.
If you have to sit the second opportunity (July), the marks of any of the assessed parts (final exam, practical work and tutored work) will be retained, provided that they are equal to or higher than 5, having to repeat the parts or assessed concepts that do not reach a 5.
The grade will not be retained for any of the parts or concepts assessed from one academic year to the next, regardless of the grade obtained in them.
It is considered that the hours of dedication of the students in relation to the different contents of the subject: theoretical, interactive and laboratory, will be, respectively: 50 hours, 17 hours and 25 hours, taking into account that the hours of classes in these three modalities will also be, respectively: 10, 6 and 5.
Although there are theoretical contents, these are fundamentally aimed at providing the necessary knowledge to make the most of the interactive contents and the laboratory practices. Therefore, not only the teaching staff, but also the students must see the subject as a whole as contents that are reinforced, understood and applied through multiple cross-interactions.
On the other hand, it is important that the students make use of the material that the teaching staff will be adding to the virtual campus, which includes a large number of practical and real cases.
José Manuel Cotos Yáñez
- Department
- Electronics and Computing
- Area
- Languages and Computer Systems
- Phone
- 881816461
- manel.cotos [at] usc.es
- Category
- Professor: University Lecturer
Senén Barro Ameneiro
Coordinador/a- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881816469
- senen.barro [at] usc.es
- Category
- Professor: University Professor
| Thursday | |||
|---|---|---|---|
| 18:30-20:00 | Grupo /CLE_01 | English | IA.12 |
| 01.23.2025 10:30-14:00 | Grupo /CLIL_01 | IA.12 |
| 01.23.2025 10:30-14:00 | Grupo /CLE_01 | IA.12 |
| 07.03.2025 10:30-14:00 | Grupo /CLE_01 | IA.12 |
| 07.03.2025 10:30-14:00 | Grupo /CLIL_01 | IA.12 |