Computational Intelligence

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Credits
5
Types
Compulsory
Requirements
This subject has not requirements, but it has got previous capacities
Department
CS
The objective of this course is to provide the theoretical and practical knowledge necessary for students to be able to efficiently design and implement intelligent systems within the field of Computational Intelligence. Specifically, students will acquire the basic concepts of neural computing, evolutionary computing and fuzzy computing.

Teachers

Person in charge

  • Maria Angela Nebot Castells ( )

Others

  • Enrique Romero Merino ( )
  • Luis Antonio Belanche Muñoz ( )

Weekly hours

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

Competences

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.

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.

Transversal Competences

Information literacy

  • 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: CEA8, CG3, CEP2, CEP3, CT4, CT5,
  4. Decide, defend and criticize a solution to a computational intelligence problem, arguing on the strengths and weaknesses of the chosen approach
    Related competences: CEA4, CEA8, CG3, CEP2, CEP3, CT4, CT5,
  5. Learn the fundamentals of neural computation and apply them effectively to develop correct and efficient solutions to a computational intelligence task
    Related competences: CEA4, CEP2, CEP3, CT5,
  6. Learn the fundamentals of evolutionary computation and apply them correctly to develop correct and efficient solutions to computational intelligence tasks.
    Related competences: CEA4, CEP2, CEP3, CT5,
  7. Learn the fundamentals of fuzzy computation and apply them correctly to develop correct and efficient solutions to computational intelligence tasks
    Related competences: CEA4, CEP2, CEP3, CT5,

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 hybrid.
  5. Applications and case studies
    Applications and case studies on real problems in regression, classification, identification and system optimization

Activities

Activity Evaluation act


Development of topic 1 of the course

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

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.
Objectives: 2 5
Contents:
Theory
9h
Problems
0h
Laboratory
3h
Guided learning
0h
Autonomous learning
12h

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.
Objectives: 2 6
Contents:
Theory
9h
Problems
0h
Laboratory
3h
Guided learning
0h
Autonomous learning
12h

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.
Objectives: 2 7
Contents:
Theory
9h
Problems
0h
Laboratory
3h
Guided learning
0h
Autonomous learning
12h

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.
Objectives: 1 3 4
Theory
6h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Execution and delivery of practical work


Objectives: 1 2 3 4 5 6 7
Week: 15
Type: assigment
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
35h

Exam

It is a written test on the knowledge of the fundamental concepts of the course

Week: 15 (Outside class hours)
Type: final exam
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

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.

These concepts are reflected in the practical work that must be delivered at the end of the course. There are three laboratory sessions serve to reinforce the theoretical concepts introduced in the lectures as well as to prepare for the practical work. This practical work requires the student to pick a real problem that collects and integrates the knowledge and skills of the course. There is also a written test of essential knowledge of the subject. In addition, there are 3 small practical exercises after each laboratory class.

Evaluation methodology

The course is scored as follows:

NLab1 = Score of laboratory exercises 1
NLab2 = Score of laboratory exercises 2
NLab3 = Score of laboratory exercises 3
NExam = Score of the exam
NPract = Score for the practical work

NFINAL = 5% NLab1 + 5% NLab2 + 5% NLab3 + 50% NExam + 35% NPract

Bibliography

Basic:

Complementary:

Web links

Previous capacities

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