IDENTIFYING DATA 2019_20
Subject (*) ARTIFICIAL INTELLIGENCE Code 17234128
Study programme
Bachelor's Degree in Computer engineering (2010)
 Cycle 1st
Descriptors Credits Type Year Period
6 Compulsory Fourth 1Q
Language
Català
Department Computer Engineering and Mathematics
Coordinator
MORENO RIBAS, ANTONIO
 E-mail antonio.moreno@urv.cat
david.sanchez@urv.cat
Lecturers
MORENO RIBAS, ANTONIO
SÁNCHEZ RUENES, DAVID
Web http://moodle.urv.cat
General description and relevant information Introducció als conceptes i les tècniques bàsiques de la Intel.ligència Artificial (IA).

Competences
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.
Type B Code Competences Transversal
Type C Code Competences Nuclear

Learning outcomes
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

Contents
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. Production Systems. Ontologies.
Knowledge-based systems. Architecture of a Knowledge-Based System: knowledge base and inference engine. Working cycle of an inference engine. Use of Machine Learning techniques.


Planning
Methodologies  ::  Tests
  Competences (*) Class hours
 Hours outside the classroom
 (**) Total hours
Introductory activities
2 0 2
Lecture
CM15
CP4
CP5
CP7
 13 28 41
IT-based practicals in computer rooms
CM15
CP4
CP5
CP7
 28 75 103
Personal attention
0 0 0
 
Short-answer objective tests
CM15
CP3
CP4
 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
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.

Personalized attention
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. Although this course is not offered in English, foreign exchange students will receive personalised support in English and will be able to develop the evaluation activities in this language.

Assessment
Methodologies Competences Description Weight        
IT-based practicals in computer rooms
CM15
CP4
CP5
CP7
 Development of practical exercises where AI techniques are applied.
50%
Short-answer objective tests
CM15
CP3
CP4
 Written exams with short questions about the basic methods used in AI. 50%
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.

Sources of information

Basic Russell, S.; Norvig, P., Artificial Intelligence. A modern approach (3a ed), Prentice Hall, 2010

Complementary 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

Recommendations


Subjects that it is recommended to have taken before
DATA STRUCTURES/17234115
 
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.