Type A
|
Code |
Competences Specific |
Type B
|
Code |
Competences Transversal |
Type C
|
Code |
Competences Nuclear |
Type A
|
Code |
Learning outcomes |
Type B
|
Code |
Learning outcomes |
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. The students of AI (ETIS/ETIG, in extinction) will also follow the same evaluation. |
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 |
PROGRAMMING METHODOLOGIES/17234116 | DATA STRUCTURES/17234115 |
|
|
Other comments |
The students of Artificial Intelligence (ETIS, ETIG, in extinction) can attend the lectures of this course in GEI and they will have the same content and evaluation. This course is recommended for students that want to study the Master on Computer Engineering: Computer Security and Intelligent Systems 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. |
|