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. |
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 |
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. |
|