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
Areas: Computer Science and Artificial Intelligence
Center Higher Technical Engineering School
Call: First Semester
Teaching: With teaching
Enrolment: Enrollable | 1st year (Yes)
The course introduces the student to the basic aspects that define AI, mainly the automatic resolution of problems that are not or hardly approachable by conventional programming techniques. In this context, state space search algorithms for problem solving will be addressed, as well as knowledge representation and reasoning.
Learning outcomes: To know the fundamental principles and basic techniques of artificial intelligence.
1. Introduction.
2. Problem solving in AI.
3. Structured representations of knowledge.
4. Methods of knowledge representation.
5. Basic models of reasoning.
BASIC BIBLIOGRAPHY
- Russell, S., Norvig, P. Artificial Intelligence (A Modern Approach), (4th Edition Global Edition, 2022). ISBN: 9781292401133.
- R. Marín, J.T. Palma, Inteligencia Artificial. Técnicas, Métods y aplicaciones. McGraw-Hill, 2008. ISBN 978-84-481-5618-3.
- Fernández Galán, S., González Boticario, J., Mira Mira, J. Problemas resueltos de inteligencia artificial aplicada: búsqueda y representación. Addison Wesley (1998). ISBN: 9788478290178
COMPLEMENTARY BIBLIOGRAPHY
- Nilsson, N.J. Artificial Intelligence (A New Synthesis). McGraw-Hill (2001). ISBN: 9788448128241
- Virginie Mathivet. Inteligencia Artificial para desarrolladores. ENI Editions, 2015.
- Fernando Sancho Caparrini. Curso de Inteligencia Artificial. http://www.cs.us.es/~fsancho
The course contributes to the development of the following general and specific competences included in the degree report:
BASIC AND GENERAL
CG1 - Maintaining and extending grounded theoretical approaches to enable the introduction and exploitation of new and advanced technologies in the field of Artificial Intelligence.
CG3 - Search and select the useful information needed to solve complex problems, handling with fluency the bibliographic sources of the field.
CB6 - 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.
CB8 - Students are able to integrate knowledge and deal with the complexities of making judgements based on incomplete or limited information, including reflections on the social and ethical responsibilities associated with the application of their knowledge and judgements.
TRANSVERSAL
CT2 - Master the oral and written expression and comprehension of a foreign language.
CT3 - Use the basic tools of information and communication technologies (ICT) necessary for the exercise of their profession and for lifelong learning.
CT4 - To develop for the exercise of a citizenship that respects democratic culture, human rights and the gender perspective.
CT6 - Acquire life skills and healthy habits, routines and lifestyles.
CT8 - To value the importance of research, innovation and technological development in the socio-economic and cultural progress of society.
SPECIFIC
CE4 - To know the fundamentals and basic techniques of artificial intelligence and its practical application.
The course will use the Virtual Campus of the three universities as the basic platform (contents repository and virtual tutoring of students). In the virtual class of the course students will have access to all the materials (theoretical materials, class slides, labs scripts, ...). The didactic methodology will be based essentially on individual work, although sometimes it will be developed in groups, mainly in discussion with the teaching staff in expository and interactive classes.
Theoretical sessions (face to face at USC, streamed online for UdC and UVIGO students): for each topic or thematic block of the lectures, the teacher will prepare the contents, explain the objectives of the topic to the students in class, suggest bibliographical resources and provide additional work material, mainly exercises related to the theoretical concepts. In addition, the teacher will propose to the students a set of activities to be carried out individually or in groups (cases, exercises) that the students must submit for evaluation, according to the deadlines.
The interactive sessions will take place face to face in the IT lab, using different software tools and developing applications for each of the thematic blocks.
Students will work individually or in small groups, with constant monitoring and tutoring from the teacher. Practice scripts will be provided with the tasks to be carried out individually or in small groups.
The learning assessment considers both the theoretical and the practical part. In order to pass the subject an overall mark equal to or higher than 5 must be obtained, out of a maximum of 10 points in the assessment activities, whose weight in the final assessment will be within the ranges included in the degree report:
E1: Final exam 50%
E2: Evaluation of practical work 50%.
Students who have not taken the exam and have not submitted to the evaluation of any other compulsory activity will obtain the grade of not presented.
In order to pass the course in the second opportunity, students must submit to the evaluation of all those parts or pending compulsory deliveries that are established. For the rest, the grades obtained during the course will be retained.
In the case of fraudulent performance of exercises or tests, it will apply the provisions contained in the rules of evaluation of the academic performance of students and review of grades (https://www.xunta.gal/dog/Publicados/2011/20110721/AnuncioG2018-190711-…). In application of the regulations of the ETSE on plagiarism (approved by the Xunta da ETSE on 19/12/2019), the total or partial copy of any exercise of practices or theory will mean the failure of the two opportunities of the course, with the qualification of 0.0 in both cases (https://www.usc.es/etse/files/u1/NormativaPlagioETSE2019.pdf).
Classroom work time: 21 total hours, divided into 10h (theory classes), 7h (practical laboratory classes), 4h (problem-based learning, seminars, case studies and projects).
Personal work time: 54h (total).
We recommend students to solve, implement, verify, and validate all the proposed exercises and practices (not only the evaluable ones). It is also considered important to make an intense use of the tutorials for the resolution of doubts and an active participation in the expository and interactive sessions.
This course will be taught in English.
The theoretical classes will be delivered by USC, face to face for USC students and streamed online for UdC and UVIGO students.
Interactive classes at USC will also be face to face in an IT lab.
Manuel Lama Penin
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881816427
- manuel.lama [at] usc.es
- Category
- Professor: University Professor
David Chaves Fraga
Coordinador/a- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881815525
- david.chaves [at] usc.es
- Category
- Professor: LOU (Organic Law for Universities) PhD Assistant Professor
Nikolay Babakov
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- nikolay.babakov [at] usc.es
- Category
- Predoutoral Marie Curie
| Thursday | |||
|---|---|---|---|
| 17:00-18:30 | Grupo /CLE_01 | English | IA.02 |
| 18:30-20:00 | Grupo /CLIL_01 | English | IA.02 |
| 12.19.2024 16:00-20:00 | Grupo /CLIL_01 | IA.02 |
| 12.19.2024 16:00-20:00 | Grupo /CLE_01 | IA.02 |
| 06.23.2025 16:00-20:00 | Grupo /CLIL_01 | IA.02 |
| 06.23.2025 16:00-20:00 | Grupo /CLE_01 | IA.02 |