Universidad Pública de Navarra - Nafarroako Univertsitate Publikoa
Public University of Navarre
| Course code: 240602 | Subject title: COMPUTATION | ||||
| Credits: 6 | Type of subject: Mandatory | Year: | Period: 2º S | ||
| Department: Estadística, Informática y Matemáticas | |||||
| Lecturers: | |||||
| FERNANDEZ FERNANDEZ, FCO. JAVIER (Resp) [Mentoring ] | DE MIGUEL TURULLOLS, LAURA [Mentoring ] | ||||
Module/Subject matter
Contents
This subject aims to provide the student with the basic principles and concepts of classical and evolutionary computing with a theoretical and engineering-oriented approach. They should acquire a solid foundation in the handling of some classical methods, genetic algorithms and bio-inspired algorithms. They should be able to solve optimization problems with these algorithms.
General proficiencies
G1 - Ability to conceive, draft, organize, plan, develop and sign projects in the field of computer engineering aimed at the conception, development or exploitation of computer systems, services and applications.
G10 - Knowledge for carrying out measurements, calculations, valuations, appraisals, expert opinions, studies, reports, task planning and other similar work in computer science.
G4 - Ability to define, evaluate and select hardware and software platforms for the development and execution of computer systems, services and applications.
G6 - Ability to conceive and develop centralized or distributed computer systems or architectures integrating hardware, software and networks.
G9 - Ability to solve problems with initiative, decision-making, autonomy and creativity. Ability to communicate and transmit the knowledge, skills and abilities of the profession of Technical Engineer in Computer Science.
T1 - Analysis and synthesis skills
T3 - Oral and written communication
T4 - Problem solving
T5 - Decision making
T6 - Teamwork
Specific proficiencies
C1 - Ability to have a deep understanding of the fundamental principles and models of computing and to know how to apply them to interpret, select, evaluate, model, and create new concepts, theories, uses and technological developments related to computer science.
C3 - Ability to evaluate the computational complexity of a problem, to know algorithmic strategies that can lead to its solution and to recommend, develop and implement the one that guarantees the best performance according to the established requirements.
C4 - Ability to acquire, obtain, formalize and represent human knowledge in a computable form for problem solving through a computer system in any field of application, particularly those related to aspects of computation, perception and action in intelligent environments or environments.
C5 - Ability to know and develop computational learning techniques and design and implement applications and systems that use them, including those dedicated to automatic information and knowledge extraction from large volumes of data.
Learning outcomes
RA1 - To learn some of the fundamental computational techniques from both a classical and evolutionary perspective, understanding their differences.
RA2 - Analyze and identify in which situations these techniques can be used.
RA3 - Design solutions to specific problems using the studied evolutionary computing techniques.
Methodology
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Methodology- Activity
|
In-class hours
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Personal hours
|
|
A-1 Theoretical lessons
|
22 |
|
|
A-2 Lerning based on problems
|
8 |
|
|
A-3 Practical sessions
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24
|
|
|
A-4 Exercises
|
|
25 |
|
A-5 Reports
|
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35
|
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A-6 Individual study
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25 |
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A-7 Exams
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8 |
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A-8 Tutorial hours in small groups
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3
|
|
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Total
|
65
|
85
|
Evaluation
| Learning outcome |
Assessment activity |
Weight (%) | It allows test resit |
Minimum required grade |
|---|---|---|---|---|
| RA1 - RA3 | Written exam | 50 | yes | 5 |
| RA1 - RA3 | Lab tasks | 50 | yes |
Note: In order to pass the subject, it is necessary to pass the individual written final exam (section 1).
In case of failing said exam, the final grade of the subject will be the one obtained in said exam.
The extraordinary exam recovers 100% of the subject. For those students who must take said exam, the final grade of the subject will be the one obtained in it.
Agenda
1.- Introduction to classical computing methods.
2.- Optimization and zero location problems
3.- Genetic and bio-inspired computing
4.- Other bio-inspired computing models.
5.- Problem completeness.
Bibliography
Access the bibliography that your professor has requested from the Library.
J. Koza. Programación genética. MIT Press, 1992
D. E. Goldberg, Algoritmos genéticos en búsquedas, optimización y aprendizaje. Addison-Wesly, 1989
Cazorla et al. Técnicas de Inteligencia Artificial. Serv. publicaciones U.A. Cap. 11.
T. Mitchell. Machine Learning. McGraw-Hill, 1997. Cap 9.