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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

PHM113 AND CSE141

Description

  • English Description

    Foundations and definition of computational intelligence. Symbolic learning, Statistical learning. Artificial neural networks: Supervised learning, Unsupervised learning, Competitive learning systems. Support Vector Machines (SVMs). Cluster analysis. Fuzzy systems: Fuzzy sets, Relations, Operations on fuzzy sets, Fuzzy logic, Approximate reasoning, Fuzzy control. Evolutionary computation: Genetic algorithms, Genetic programming, Genetic optimization. Particle SWSYm optimization. Hybrid intelligent methods.
  • Arabic Description

    Foundations and definition of computational intelligence. Symbolic learning, Statistical learning. Artificial neural networks: Supervised learning, Unsupervised learning, Competitive learning systems. Support Vector Machines (SVMs). Cluster analysis. Fuzzy systems: Fuzzy sets, Relations, Operations on fuzzy sets, Fuzzy logic, Approximate reasoning, Fuzzy control. Evolutionary computation: Genetic algorithms, Genetic programming, Genetic optimization. Particle SWSYm optimization. Hybrid intelligent methods.
  • Department
    Computer and Systems Engineering
  • Credit Hours
    3
  • Grades
    Total ( 100 ) = Midterm (25) + tr.Major Assessment (30 = tr.Industry 0% , tr.Project 15% , tr.Self_learning 5% , tr.Seminar 15% ) + tr.Minor Assessment (5) + Exam Grade (40)
  • Hours
    Lecture Hours: 2, Tutorial Hours: 2, Lab Hours: 0
  • Required SWL
    125
  • Equivalent ECTS
    5
  • 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