Computational Intelligence

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Credits
5
Department
CS
Types
Compulsory
Requirements
This subject has not requirements
The aim of this course is to provide the students with the knowledge and skills required to design and implement effective and efficient Computational Intelligence solutions to problems for which a direct solution is impractical or unknown. Specifically, students will acquire the basic concepts of fuzzy, evolutionary and neural computation. The student will also apply this knowledge to solve some real case studies.

Teachers

Person in charge

  • Luis Antonio Belanche Muñoz ( )

Others

  • Maria Angela Nebot Castells ( )

Weekly hours

Theory
2.4
Problems
0
Laboratory
0.6
Guided learning
0.21
Autonomous learning
5.1

Competences

Technical Competences of each Specialization

Academic

  • CEA4 - Capability to understand the basic operation principles of Computational Intelligence main techniques, and to know how to use in the environment of an intelligent system or service.
  • CEA8 - Capability to research in new techniques, methodologies, architectures, services or systems in the area of ??Artificial Intelligence.

Professional

  • CEP2 - Capability to solve the decision making problems from different organizations, integrating intelligent tools.
  • CEP3 - Capacity for applying Artificial Intelligence techniques in technological and industrial environments to improve quality and productivity.

Generic Technical Competences

Generic

  • CG3 - Capacity for modeling, calculation, simulation, development and implementation in technology and company engineering centers, particularly in research, development and innovation in all areas related to Artificial Intelligence.

Transversal Competences

Solvent use of the information resources

  • CT4 - Capacity for managing the acquisition, the structuring, analysis and visualization of data and information in the field of specialisation, and for critically assessing the results of this management.

Appropiate attitude towards work

  • CT5 - Capability to be motivated for professional development, to meet new challenges and for continuous improvement. Capability to work in situations with lack of information.

Objectives

  1. Know the scope of C​omputational Intelligence (CI), and the types of tasks that can be tackled with CI methods
    Related competences: CEA4, CG3,
  2. Know the most important modern computational intelligence techniques
    Related competences: CEA4, CEA8, CG3, CEP2,
  3. Organize the problem solving flow for a computational intelligence problem, analyzing the possible options and choosing the most appropriate techniques or combinations of techniques
    Related competences: CT4, CT5, CEA8, CG3, CEP2, CEP3,
  4. Decide, defend and criticize a solution to a computational intelligence problem, arguing on the strengths and weaknesses of the chosen approach
    Related competences: CT4, CT5, CEA4, CEA8, CG3, CEP2, CEP3,
  5. Learn the fundamentals of neural computation and apply them effectively to develop correct and efficient solutions to a computational intelligence task
    Related competences: CT5, CEA4, CEP2, CEP3,
  6. Learn the fundamentals of evolutionary computation and apply them correctly to develop correct and efficient solutions to computational intelligence tasks.
    Related competences: CT5, CEA4, CEP2, CEP3,
  7. Learn the fundamentals of fuzzy computation and apply them correctly to develop correct and efficient solutions to computational intelligence tasks
    Related competences: CT5, CEA4, CEP2, CEP3,

Contents

  1. Introduction to Computational Intelligence
    Computational Intelligence: definition and paradigms. Brief historical sketch.
  2. Foundations of Neural Computation
    Introduction to neural computation: biological inspiration, neural network models, architectures and training algorithms. Learning and generalization.
  3. Foundations of Evolutionary Computation
    Introduction to evolutionary computation: evolutionary processes in nature, genetic operators, evolutionary optimization algorithms. Genetic algorithms. Evolution Strategies and CMA-ES.
  4. Foundations of Fuzzy Computation
    Introduction to fuzzy computation: fuzzy sets and systems, fuzzy inference systems and FIR.
  5. Applications and case studies
    Applications and case studies on real problems in regression, classification, identification and system optimization

Activities

Development of topic 1 of the course

The teacher presents an overview and basic concepts of computational intelligence as well as modern application examples.
Theory
3
Problems
0
Laboratory
0
Guided learning
0
Autonomous learning
0
Objectives: 1 2
Contents:

Development of topic 1 of the course

The teacher presents the fundamentals of neural computing: inspiration in biological neuron models, architectures and training algorithms. The teacher explains the concepts of learning and generalization and introduces methodologies for obtaining effective models and to guarantee an honest assessment of their effectiveness.
Theory
9
Problems
0
Laboratory
3
Guided learning
0
Autonomous learning
12
Objectives: 2 5
Contents:

Development of topic 3 of the course

The professor explains the fundamentals of evolutionary computation: evolutionary processes in nature, genetic operators, evolutionary optimization algorithms. Focuses on genetic algorithms and Evolution Strategies and CMA-ES. Points to other existing evolutionary algorithms.
Theory
9
Problems
0
Laboratory
3
Guided learning
0
Autonomous learning
12
Objectives: 2 6
Contents:

Development of topic 4 of the course

The teacher explains the fundamentals of fuzzy computing: fuzzy sets and fuzzy systems, fuzzy inference systems and FIR.
Theory
9
Problems
0
Laboratory
3
Guided learning
0
Autonomous learning
12
Objectives: 2 7
Contents:

Development of topic 5 of the course

The teacher presents one or more real case studies that might require solutions from computational intelligence. The teacher looks at the options and outlines one or more possible solutions, discussing their advantages and disadvantages. The teacher presents the course work that must be carried out, which is similar to previous case studies.
Theory
6
Problems
0
Laboratory
0
Guided learning
0
Autonomous learning
0
Objectives: 1 3 4
Contents:

Teaching methodology

The topics exposed in the lectures are very well motivated (why is this important?) and motivating (why is this relevant nowadays?) and supplemented with many real examples. These lectures will introduce all the knowledge, techniques, concepts and results necessary to achieve a solid understanding of the fundamental concepts and techniques. There are three laboratory sessions serve to reinforce the theoretical concepts introduced in the lectures as well as to prepare for the practical work to be delivered at the end of the course.

This practical work requires the student to pick a real problem that collects and integrates the knowledge and skills of the course. In addition there is a written test of basic knowledge.

Evaluation methodology

The course is scored as follows:

NPract = Score for the practical work
NExam = Score of the exam
NUSRI = Score of the generic skill USE OF INFORMATION RESOURCES

NFINAL = 25% NExam + 70% NPract + 5% NUSRI

Bibliografy

Basic:

Complementary:

Web links

Previous capacities

Elementary notions of probability, statistics, linear algebra and real analysis