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Syllabus 2013-14 - 13312034 - Intelligent Information Systems (Sistemas inteligentes de información)

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  • Level 1: Tutorial support sessions, materials and exams in this language
  • Level 2: Tutorial support sessions, materials, exams and seminars in this language
  • Level 3: Tutorial support sessions, materials, exams, seminars and regular lectures in this language
DEGREE: Grado en Ingeniería informática
FACULTY: SCHOOL OF ENGINEERING OF JAÉN
ACADEMIC YEAR: 2013-14
COURSE: Intelligent Information Systems
SYLLABUS
1. COURSE BASIC INFORMATION
NAME: Intelligent Information Systems
CODE: 13312034 ACADEMIC YEAR: 2013-14
LANGUAGE: English LEVEL: 2
ECTS CREDITS: 6.0 YEAR: 3 SEMESTER: SC
2. LECTURER BASIC INFORMATION
NAME: GARCÍA LÓPEZ, SALVADOR
DEPARTMENT: U118 - INFORMÁTICA
FIELD OF STUDY: 075 - CIENCIA DE LA COMPUTACIÓN E INT. ARTIFICIAL
OFFICE NO.: A3 - 246 E-MAIL: sglopez@ujaen.es P: 953212802
WEBSITE: -
ORCID: https://orcid.org/0000-0003-4494-7565
LANGUAGE: English LEVEL: 1
3. CONTENT DESCRIPTION

Module 1. Introduction to the Knowledge Representation

  • Acquirement and Representation of Knowledge
  • Knowledge Engineering. Ontologies.

Module 2. Basic Models of Knowledge Representation

  • Logic based Models. First Order Logic.
  • Inference in First Order Logic.
  • Rule based Systems.
  • Structured Models. Semantic Networks.

Module 3. Advanced Models of Knowledge Representation

  • Uncertainty Representation. Bayesian Networks.
  • Fuzzy Logic based Systems.
  • Introduction to Big Data.

Module 4. Machine Learning

  • Introduction, Models and Validation in Machine Learning.
  • Crisp and Fuzzy Rules based Learning Systems.
  • Bayesian Learning.
  • Neural Networks and Kernels.

4. COURSE DESCRIPTION AND TEACHING METHODOLOGY

This subject is divided into two related parts: Theoretical contents and Practical activities.

The theoretical part will be mainly taught through lectures, including the solving of exercises, doubts and discussions which will be taken into account in the evaluation of participacion of the student in the subject.

The practical part of the subject will be held in the laboratories.

Students with special educational needs should contact the Student Attention Service (Servicio de Atención y Ayudas al Estudiante) in order to receive the appropriate academic support

5. ASSESSMENT METHODOLOGY

To pass the course, it is necessary to independently pass the theoretical part and the practical part.

The theoretical part will be evaluated through an objective test of theoretical concepts and the accomplishment of practical exercises related with the subject. The practical part will be evaluated by providing some works performed with the computer and a justificative report associated to them. The evaluation of the practical activities will be continuous and incremental, in the way that both, the presentation of the final practical proyect provided and the evolution of the student throughout the practical sessions, wil be taken into account.

There will also be attendance control to both lectures and practical sessions.

6. BOOKLIST
MAIN BOOKLIST:
  • Introduction to machine learning. Edition: 2nd ed.. Author: Alpaydin, Ethem.. Publisher: Cambridge, Mass. : MIT Press, 2010  (Library)
  • Fuzzy set theory and its applications . Edition: -. Author: Zimmermann, H.-J.. Publisher: Boston : Kluwer Academic Publishers, cop. 2001  (Library)
  • Artificial intelligence: a modern approach. Edition: 3rd th. Author: Russell, Stuart J.. Publisher: Boston [etc.] : Pearson Education, 2010  (Library)
ADDITIONAL BOOKLIST:
  • Hadoop [Recurso electrónico] : the definitive guide. Edition: 3rd ed. Author: White, Tom (Tom E.). Publisher: Sebastopol, CA : O'Reilly, 2012  (Library)
  • Pattern Recognition and Machine Learning [Recurso electrónico]. Edition: -. Author: Bishop, Christopher M.. Publisher: New York, NY : Springer Science+Business Media, LLC, 2006.  (Library)
  • Bayesian artificial intelligence. Edition: 2nd ed.. Author: Korb, Kevin B.. Publisher: Boca Raton, FL : CRC Press, c2011  (Library)