Public University of Navarre



Castellano | Academic year: 2024/2025 | Previous academic years:  2023/2024  |  2022/2023 
Bachelor's degree in Computer Science at the Universidad Pública de Navarra
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 ]

Partes de este texto:

 

Module/Subject matter

Module: Mandatory module of Computation and Intelligent Systems
Subject matter: Computation

 

 

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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.

 

 

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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

 

 

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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.

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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.

 

 

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Methodology

Methodology- Activity
In-class hours
Personal hours
A-1 Theoretical lessons

22
 
A-2 Lerning based on problems

8
 
A-3 Practical sessions
 24
 
A-4 Exercises
 
 25
A-5 Reports
 
 35
A-6 Individual study
 

25
A-7 Exams

8
 
A-8 Tutorial hours in small groups
 3
 
Total
65
85

 

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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.

 

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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.

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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.

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Languages

Spanish

English

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Location

Arrosadia Campus

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