ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Student's work ECTS: 99 Hours of tutorials: 3 Expository Class: 24 Interactive Classroom: 24 Total: 150
Use languages Spanish, Galician
Type: Ordinary Degree Subject RD 1393/2007 - 822/2021
Departments: Electronics and Computing
Areas: Computer Science and Artificial Intelligence
Center Higher Technical Engineering School
Call:
Teaching: Sin docencia (Extinguida)
Enrolment: No Matriculable
Obtain basic knowledge about Artificial Intelligence: its evolution, types of problems it addresses and techniques to solve them, social, economic, ethical and employment repercussions...
Lectures:
T1: Historical perspective of AI.
Strategies for AI problem solving
T2: State space search and metaheuristics
T3: Knowledge-based
T4: Connectionist systems
Machine learning
T5: Linear and logistic regression
T6: Supervised learning
T7: Unsupervised learning
T8: Repercussions of AI: socioeconomic, ethical...
Interactive classes:
CI1: Interactive seminars on various applications, both in operation and in research.
CI2: Oral communication seminar
CI3: Discussions on topics related to the social, economic, ethical... impact of AI.
Writing competition on particularly topical issues in AI -optional-.
Practicals:
P1: Metaheuristics
P2: Knowledge-based systems
P3: Machine learning
Translated with www.DeepL.com/Translator (free version)
1. J. Palma, R. Marín (eds.). Inteligencia Artificial. Métodos, técnicas y aplicaciones. McGrawHill. (2008). ISBN: 9788448156183
2. Fernández Galán, S., González Boticario, J., Mira Mira, J. Problemas Resueltos de Inteligencia Artificial Aplicada. Búsqueda y Representación. Addison Wesley. (1998). ISBN: 9788478290178
3. Russell, S., Norvig, P. Inteligencia Artificial (Un Enfoque Moderno), Segunda ed. Prentice-Hall International. (2004). ISBN: 9789688806821
4. Nilsson, N.J. Inteligencia artificial (Una nueva síntesis). McGraw-Hill. (2001). ISBN: 9788448128241
5. Gendreau, Michel, Potvin, Jean-Yves. Handbook of Metaheuristics. Springer-Verlag. (2010). ISBN: 978-1-4419-1665-5
6. Ian Goodfellow, Yoshua Bengio and Aaron Courville, “Deep Learning”, An MIT Press book, 2016
7. Simon O. Haykin, "Neural Networks and Learning Machines", Prentice Hall, 3rd edition, 2008. ISBN 0131471392
Contribute to achieve the competences included in the memory of the Degree in Computer Engineering of the USC (CG4, CG8, CG9, TR1-3, RI6-7, RI15, TI1), especially:
CX4- Ability to select the most appropriate CE technique for the development of applications or services.
CX8- Ability to develop new methods or technologies using CE
CX9- Ability to analyze and solve problems applying CE with initiative, autonomy and creativity.
TR1- Capacity of analysis and synthesis. Capacity for organization and planning. Problem solving. Decision making.
TR2- Critical reasoning.
TR3. Systemic: Autonomous learning. Creativity.
RI6-7- Knowledge and application of the algorithmic procedures of CE to design solutions to problems, using efficiently the most appropriate data structures.
RI15- Practical application of intelligent systems based on explicitly formalized knowledge.
TI1- Ability to understand the needs of an organization in the field of CE.
The subject contributes to achieve the competences related to the module "Intelligent Systems", also detailed in the memory of the Degree, especially those referred to:
CMSI4- Know, understand and handle problem solving techniques based on a knowledge representation.
CMSI5- Know, understand and know how to apply knowledge-based systems design methodologies.
The didactic methodology will be based on individual work -although sometimes in groups-, discussion with the teacher in class and individual tutorials.
For each topic or thematic block of the expository classes, the teacher will prepare the contents, explain the objectives of the topic to the students in class, suggest bibliography, provide them with additional work material, etc. In the expository classes the competences CX4, CX9, TR1, RI15, TI1, CMSI4-5 will be worked on. In addition, the professors will propose to the students a set of activities to be carried out, individually or in groups (works, presentations, readings, practices...) The students will generally have to present them to the professor for their evaluation, for which the deadlines for delivery/presentation will be indicated through the channels used for student-professor communication. These activities will allow the development of the competences CX4, CX8, CX9, TR1-3, RI15, CMSI4-5.
The practices and part of the interactive sessions will be developed in the Computer Room of the School, using different software tools for each of the thematic blocks. The realization of the practices will allow to develop the competences CX4, CX8, TR1-3, RI6-7, RI15, CMSI4-5.
Students will work individually or in small groups, with the constant support of the teacher. There will be scripts of practices, seminars and other activities to be carried out individually or in small groups.
The teaching will be supported by the virtual USC platform as follows: repository of documentation related to the subject (texts, presentations, recommended readings...) and virtual tutoring of students (e-mail, forums).
In the event that due to COVID-19 the USC determines the passage to scenario 2 (distancing) or scenario 3 (closure of facilities), the teaching methodology will be modified according to the contingency plan indicated in the "Observations" section.
The learning evaluation considers both the theoretical part (40%), the practical part (40%) and the interactive activities (20%). In order to pass the subject an overall grade of 5 or higher must be obtained, out of a maximum score of 10 points, according to the following criteria:
- Theoretical part: it will be evaluated in a single exam to be taken on the official date. The grade of the exam must be equal or higher than 4 out of a maximum score of 10 points, in order to pass the whole subject. Otherwise it will have to be repeated in the opportunity of recovery.
- Practical part: evaluation of all the practical activities proposed by the teachers (delivery of works, presentations in the classroom, delivery of exercises, realization of practices,...). All the practical activities will have the same weight in the practical grade. The grade of this part must be equal or higher than 4 out of a maximum score of 10 points, in order to pass the whole subject. Otherwise, at least those practices with a grade lower than 3 points must be evaluated in the opportunity of recovery.
- Interactive activities: evaluation of all the compulsory interactive activities proposed by the teacher (delivery of assignments, presentations and participation in the classroom, delivery of exercises,...). The compulsory interactive activities will be evaluated globally on a maximum of 10 points and the points awarded for the completion of voluntary activities may be added to the result, having in any case 10 as the maximum final score for this part. The grade for this part must be equal to or higher than 4 out of a maximum score of 10 points in order to pass the whole subject. Otherwise, this part will have to be evaluated in the opportunity of recovery in case of not having obtained a grade of at least 3 points in it.
The final grade of the subject will be the arithmetic average weighted by the percentages indicated above of the theoretical and practical parts and complementary activities. In case of incurring in any of the situations indicated above for not reaching in one or more parts the minimum grade necessary to pass the subject globally, the final grade of the opportunity will be the minimum of the grades obtained in those parts.
Students who have not taken the exam or 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 July, students must submit to the evaluation of all those compulsory parts pending, in accordance with the above specified. 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 for the evaluation of the academic performance of students and the review 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 copy of any exercise of practices or theory will mean the failure of the two opportunities of the course, with the grade of 0.0 in both cases (https://www.usc.es/etse/files/u1/NormativaPlagioETSE2019.pdf).
In the event that due to COVID-19 the USC determines the passage to scenario 2 (distancing) or scenario 3 (closure of facilities), the teaching methodology will be modified according to the contingency plan indicated in the "Observations" section.
Classroom work time: 58 total hours, divided into 15h (lectures), 35h (seminars and practicals), 3h (tutorials) and 5h (controls).
Personal work time: 92h (total), divided into 62h (independent study of theory and practices) and 30h (assignments, projects and other activities).
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 an intense use of tutorials for the resolution of doubts.
The Virtual Campus of the USC will be used as a tool to support the learning process in the following aspects: repository of materials (transparencies, exercises, complementary texts...), delivery and commented evaluation of compulsory works and virtual tutoring (e-mail and forums).
CONTINGENCY PLAN
In case the health situation makes it advisable to establish a Scenario 2 (distancing):
1) all expository classes will be taught online (synchronously via Microsoft Teams or asynchronously through the publication of videos recorded by the faculty),
2) the interactive classes will be taught face-to-face in the computer classroom,
3) the weighting of the different parts of the subject and the requirements to pass the subject will remain unchanged,
4) the final exam will be given in person.
In the event that the health situation makes it advisable to establish a Scenario 3 (closure of facilities):
1) all expository classes will be taught online (synchronously via Microsoft Teams or asynchronously through the publication of videos recorded by the faculty),
2) all interactive classes will be taught online (synchronously through Microsoft Teams or asynchronously through the publication of videos recorded by the teachers),
3) the weighting of the different parts of the subject and the requirements for passing the subject will remain unchanged,
4) the final test will be conducted offline, using Microsoft Teams and the Moodle virtual classroom tools.
Alberto Jose Bugarin Diz
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881816440
- alberto.bugarin.diz [at] usc.es
- Category
- Professor: University Professor
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
Yago Fontenla Seco
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- yago.fontenla.seco [at] usc.es
- Category
- Ministry Pre-doctoral Contract
Lorenzo Vaquero Otal
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- lorenzo.vaquero [at] rai.usc.es
- Category
- Ministry Pre-doctoral Contract
Alejandro Catala Bolos
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- alejandro.catala [at] usc.es
- Category
- Professor: LOU (Organic Law for Universities) PhD Assistant Professor