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Type A
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Code |
Competences Specific |
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Type B
|
Code |
Competences Transversal |
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Type C
|
Code |
Competences Nuclear |
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Type A
|
Code |
Learning outcomes |
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Type B
|
Code |
Learning outcomes |
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Type C
|
Code |
Learning outcomes |
| Topic |
Sub-topic |
| Introduction. |
What is Artificial Intelligence? History of Artificial Intelligence.
|
| Problem solving and search. |
Problem and space state. Uninformed search. Heuristic search. Constraint satisfaction. Game playing. |
| Knowledge representation. |
Characteristics of a knowledge representation system. Logical formalisms. Frame systems. Production Systems. Ontologies. |
| Knowledge-based systems. |
Architecture of a Knowledge-Based System: knowledge base and inference engine. Knowledge acquisition. Use of Machine Learning techniques. Applications: control, monitoring, diagnosis, prediction, ...
|
| Methodologies :: Tests |
| |
Competences |
(*) Class hours
|
Hours outside the classroom
|
(**) Total hours |
| Introductory activities |
|
2 |
0 |
2 |
| Lecture |
|
13 |
28 |
41 |
| Practicals using information and communication technologies (ICTs) in computer rooms |
|
28 |
75 |
103 |
| Personal tuition |
|
0 |
0 |
0 |
| |
| Objective short-answer 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 course. Content, practical exercises, bibliography, evaluation method. |
| Lecture |
Exposition of the contents of the course. |
| Practicals using information and communication technologies (ICTs) in computer rooms |
Resolution of specific problems in the lab using the basic AI techniques explained in the lectures. |
| Personal tuition |
Personal attention to solve doubts concerning the theoretical concepts or the practical exercises.
|
|
Description |
|
Sessions in which the student can expose doubts concerning the theoretical content of the course or the design and implementation of the practical exercises. |
|
Methodologies |
Competences
|
Description |
Weight |
|
|
|
|
| Practicals using information and communication technologies (ICTs) in computer rooms |
|
Development of practical exercises where AI techniques are applied.
|
45% |
| Objective short-answer tests |
|
Proves escrites amb preguntes curtes sobre els mètodes bàsics d'IA. |
55% |
| Others |
|
|
|
| |
|
Other comments and second exam session |
|
The second call will have the same evaluation than the first one. The evaluation will be the same for all students, regardless if the course is obligatory/optional for them. |
| Basic |
Rich, E.; Knight, K., Inteligencia Artificial (3a ed), McGraw Hill, 1995
Russell, S.; Norvig, P., Artificial Intelligence. A modern approach (3a ed), Prentice Hall, 2010
|
|
| Complementary |
Giatarrano, Riley, Sistemas Expertos. Principios y Programación, International Thompson Eds., 2001
Fernández, S., González, J., Mira, J., Problemas resueltos de IA aplicada. Búsqueda y representación., Pearson-Addison Wesley, 2005
|
|
| Subjects that it is recommended to have taken before |
|
| |
| Other comments |
|
This course is recommended for students that want to study the Master on Computer Security and Artificial Intelligence Engineering or the interuniversity Master on Artificial Intelligence. |
(*)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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