Type A
|
Code |
Competences Specific | | CM15 |
Have knowledge of and apply the fundamental principles and basic techniques of intelligent systems and their practical application. |
| CP3 |
Be able to evaluate the computational complexity of a problem, know algorithmic strategies that may lead to its resolution and recommend, develop and implement the one that will guarantee the best performance in accordance with the established requirements.
|
| CP4 |
Have knowledge of the fundamentals, paradigms and techniques inherent in intelligent systems and analyse, design and construct IT systems, services and applications that use these techniques in any field of application. |
| CP5 |
Be able to acquire, obtain, formalise and represent human knowledge in a computable format to solve problems through an IT system in any field of application, particularly those related to aspects of computation, perception and action in intelligent environments and settings. |
| CP7 |
Have knowledge of and develop computational learning techniques and design and implement applications and systems that use them, including those dedicated to the automatic extraction of information and knowledge from large volumes of data.
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Type B
|
Code |
Competences Transversal |
Type C
|
Code |
Competences Nuclear |
Type A
|
Code |
Learning outcomes |
| CM15 |
List and describe the basic techniques of intelligent systems.
| | CP3 |
Know basic search and problem solving algorithms in IA.
| | CP4 |
Understand the different focusses of IA.
Know how to apply basic IA methods in the resolution of specific problems.
| | CP5 |
Know basic techniques for the representation of knowledge in intelligent systems.
| | CP7 |
Know basic methods of automatic learning.
|
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 |
IT-based practicals in computer rooms |
|
28 |
75 |
103 |
Personal attention |
|
0 |
0 |
0 |
|
Short-answer objective 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. |
IT-based practicals in computer rooms |
Resolution of specific problems in the lab using the basic AI techniques explained in the lectures. |
Personal attention |
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 |
|
|
|
|
IT-based practicals in computer rooms |
|
Development of practical exercises where AI techniques are applied.
|
45% |
Short-answer objective tests |
|
Written exams with short questions about the basic methods used in AI. |
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 use of mobiles or electronic devices in evaluation acts is not allowed. |
Basic |
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
José T.Palma, Roque Marín, Inteligencia Artificial. Técnicas, métodos y aplicaciones., McGrawHill, 2008
|
|
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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