Computational Intelligence

Course: Computational Intelligence

Code: 3ФЕИТ01007

ECTS points: 6 ECTS

Number of classes per week: 3+0+0+3

Lecturer: Asst. Prof. Dr. Vesna Ojleska - Latkoska

Course Goals (acquired competencies): The main goal of the course are the concepts, paradigms, algorithms, and ways of implementation of computational intelligence (CI), with an emphasis on their possible practical applications in engineering. Upon completion of the course, the student will gain knowledge for the basic models in CI; application of fuzzy logic, neural networks, genetic algorithms, and other algorithms in CI; use of CI techniques for solving real world problems; combining various CI techniques and selecting the most appropriate one for solution of the current problem.

Course Syllabus: Computational intelligence (CI) is a set of nature-inspired computational methodologies and approaches to address complex real-world problems to which traditional approaches, i.e., first principles modelling or explicit statistical modelling, are ineffective or infeasible. Topics that will be covered in this course are as follows: 1. Background:  Brief review of biological and behavioral motivations for the constituent methodologies of computational intelligence. 2. Relationships among the three major components of CI (evolutionary computation, neural networks, and fuzzy systems) and how they cooperate and/or are integrated into a CI system. 3. Basic concepts and paradigms of evolutionary computation: genetic algorithms, evolutionary programming, evolution strategies, and particle swarm optimization; 4. Evolutionary Computation Implementations 5. Artificial Neural Networks: Neural network components and terminology; Review of neural network topologies; Neural network learning; Hybrid networks and recurrent networks; The issues of pre-processing and post-processing. 6. Neural Network Implementations 7. Fuzzy Systems: Design and analysis of fuzzy systems; Issues and special topics related to fuzzy systems. 8. Fuzzy System Implementations 9. Computational Intelligence Implementations.

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