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Fundamentals of Computational Intelligence

What Will Learn?

  • Course Aims
    By the end of the course the students will be able to: • Understand the basic concepts and algorithms of optimization • Understand the basic concepts of training and building neural networks and their use in optimization problems • Understand the basic concepts of structure and analysis of evolutionary algorithms and their use in optimization problems • Understand the basic concepts of swarm intelligence techniques and their use in optimization problems • Understand the basic concepts of ant colony optimization and their use in optimization problems • Understand the basic concepts of fuzzy systems design and inferencing and their use in optimization problems
  • Course Goals
  • Decent Work and Economic Growth
  • Industry, Innovation and Infrastructure
  • Sustainable Cities and Communities

Requirements

PHM113s

Description

  • English Description

    Definitions, Learning theory, Soft-computing paradigm. Fuzzy systems: Fuzzy sets and relations, Operations on fuzzy sets, Fuzzy logic, Approximate reasoning, Fuzzy control. Neural networks: Machine learning using neural networks, Supervised learning, Unsupervised learning, Competitive learning systems. Evolutionary computation: Genetic algorithms, Genetic programming, Genetic optimization. Particle SWSYm optimization. Tools used in developing computational intelligence algorithms.
  • Arabic Description

    Definitions, Learning theory, Soft-computing paradigm. Fuzzy systems: Fuzzy sets and relations, Operations on fuzzy sets, Fuzzy logic, Approximate reasoning, Fuzzy control. Neural networks: Machine learning using neural networks, Supervised learning, Unsupervised learning, Competitive learning systems. Evolutionary computation: Genetic algorithms, Genetic programming, Genetic optimization. Particle SWSYm optimization. Tools used in developing computational intelligence algorithms.
  • Department
    Computer and Systems Engineering
  • Credit Hours
    2
  • Grades
    Total ( 100 ) = Midterm (20) + tr.Student Activities (30 = tr.Industry 0% , tr.Project 10% , tr.Self_learning 0% , tr.Seminar 20% ) + Exam Grade (50)
  • Hours
    Lecture Hours: 2, Tutorial Hours: 1, Lab Hours: 0
  • Required SWL
    100
  • Equivalent ECTS
    4
  • Essential books (text books)
  • • M. Wahde. Biologically Inspired Optimization Methods: An Introduction. WIT Press 2008..
  • • Computational Intelligence, Methods and Techniques, Professor Leszek Rutkowski (2008), Springer. (EKB.org)
  • Recommended books
  • Charu C. Aggarwal, "Artificial Intelligence", A Textbook, Springer, 2021