Type A
|
Code |
Competences Specific | | A1 |
Integrate the fundamental technology, applications, services and systems of Computer Security and Artificial Intelligence,in a broader, multidisciplinary context.
|
| A5 |
Analyse the information needs considered in an environment and execute all stages of the construction process of a secure information system.
|
| A9 |
Apply computational, mathematical, statistical and artificial intelligence methods in order to model, design and develop applications, services, smart systems and knowledge-based systems.
|
| G1 |
Project, calculate and design products, processes and installations in the areas of Computer Security and Artificial Intelligence
|
| G2 |
Perform mathematical modelling, calculation and simulation in company technology and engineering centres, particularly in tasks of research, development and innovation in the areas of Computer Security and Artificial Intelligence
|
Type B
|
Code |
Competences Transversal | | CT2 |
Formular valoracions a partir de la gestió i ús eficient de la informació. |
| CT3 |
Resoldre problemes complexes de manera crítica, creativa i innovadora en contextos multidisciplinars. |
| CT4 |
Treballar en equips multidisciplinars i en contextos complexes. |
| CT5 |
Comunicar idees complexes de manera efectiva a tot tipus d’audiències. |
| CT7 |
Aplicar els principis ètics i de responsabilitat social com a ciutadà i com a professional. |
Type C
|
Code |
Competences Nuclear |
Type A
|
Code |
Learning outcomes |
| A1 |
Analitza els problemes i les seves causes des d'un enfocament global i de mitjà i llarg termini.
| | A5 |
Identifica els components d'un problema de presa de decisions i saber decidir el tipus de model de presa de decisions més adequat.
| | A9 |
Dissenya tecnologies de garantia de la privacitat per a escenaris d'aplicacions informàtiques i telemàtiques.
| | G1 |
Integra els coneixements teòrics amb les realitats a les quals es poden aplicar.
| | G2 |
Aplica les tècniques apreses en contextos concrets.
|
Type B
|
Code |
Learning outcomes |
| CT2 |
Formular valoracions a partir de la gestió i ús eficient de la informació.
| | CT3 |
Resoldre problemes complexes de manera crítica, creativa i innovadora en contextos multidisciplinars.
| | CT4 |
Treballar en equips multidisciplinars i en contextos complexes.
| | CT5 |
Comunicar idees complexes de manera efectiva a tot tipus d’audiències.
| | CT7 |
Aplicar els principis ètics i de responsabilitat social com a ciutadà i com a professional.
|
Type C
|
Code |
Learning outcomes |
Topic |
Sub-topic |
1. Introduction to Multiple Criteria Decision Analysis (MCDA) |
|
2. Preference modelling |
2.1 Variables and criteria
2.2 User profile with numerical data
2.3. User profile with categorical data
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3. The Multi-Attribute Utility Theory |
3.1 Basic concepts
3.2 Aggregation operators for numerical and linguistic criteria
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4. Outranking methods for pairwise preference relation |
4.1 Basic concepts
4.2 ELECTRE method
|
5. Advanced techniques of MCDA using AI |
|
Methodologies :: Tests |
|
Competences |
(*) Class hours
|
Hours outside the classroom
|
(**) Total hours |
Introductory activities |
|
1 |
1.5 |
2.5 |
Presentations / expositions |
|
1 |
1.5 |
2.5 |
ICT practicals |
|
13 |
19.5 |
32.5 |
Material reading and studying |
|
26 |
41.5 |
67.5 |
Forums of discussion |
|
1 |
1.5 |
2.5 |
Personal tuition |
|
1 |
0 |
1 |
|
Objective multiple-choice tests |
|
2 |
2 |
4 |
|
(*) On e-learning, hours of virtual attendance of the teacher. (**) The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
Methodologies
|
Description |
Introductory activities |
Presentation of the teachers, the goals of the course and the evaluation procedure. |
Presentations / expositions |
The student will collect research papers about a certain topic (proposed by the teacher) and will make an overview and comparison. The resulting report will be evaluated. The work will also be explained in an oral presentation to the rest of students. |
ICT practicals |
The student must solvesome exercises in class (in the lab). A report will be delivered by the student, in some exercises.
|
Material reading and studying |
El professor explicarà els continguts bàsics de l'assignatura amb exemples. Posant a disposició de l'alumne tot el material que necessiti per a l'estudi de la matèria. |
Forums of discussion |
|
Personal tuition |
The student will attend questions at her office (previously arrangement by email). Questions can also be solved by email directly. |
Description |
The student will attend questions at her office (previously arrangement by email). Questions can also be solved by email directly.
|
Methodologies |
Competences
|
Description |
Weight |
|
|
|
|
Presentations / expositions |
|
Reserach work on a topic given by the lecturer. After, a personal interview about the research work must be done. A minimum grade of 5 is required in the interview to pass the course. |
30% |
ICT practicals |
|
Student will solve practical case studies using public software tools, or programming some algorithms.
|
50% |
Objective multiple-choice tests |
|
Es realitzarà una prova individual. Cal treure una nota mínima de 5 a l'examen per aprovar l'assignatura. |
20% |
Others |
|
|
|
|
Other comments and second exam session |
If any evaluation activity is failed, it should be repeated. All activities are individual. If two or more identical (or very similar) solutions are delivered by different students, it will be considered as "non valid". |
Basic |
Figueira, J., Greco, S., Ehrgott, M (eds), Multiple Criteria Decision Analysis, Springer, 2005
Ishizaka, A., Nemery, P., Multi-criteria decision analysis: methods and software, Wiley, 2013
Torra, V., Narukawa, Y., Modelling Decisions: Information fusion and Aggregation operators, Springer , 2005
|
|
Complementary |
http://www.cs.put.poznan.pl/ewgmcda/, Euro working group on MCDA, ,
http://www.mcdmsociety.org/, Int Society on MCDM, ,
Matthias Ehrgott, José Rui Figueira and Salvatore Greco, Trends in Multiple Criteria Decision Analysis, Springer, 2010
Doumpos, M., Grigoroudis, E. , Multicriteria Decision Aid and Artificial Intelligence , Wiley , 2013
|
|
(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation. |
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