| 1. | Course Title | Computational Intelligence | |||||||||||
| 2. | Code | 4ФЕИТ01007 | |||||||||||
| 3. | Study program | 21-PNMI, 6-ARSI | |||||||||||
| 4. | Organizer of the study program (unit, institute, department) | Faculty of Electrical Engineering and Information Technologies | |||||||||||
| 5. | Degree (first, second, third cycle) | Second cycle | |||||||||||
| 6. | Academic year/semester | I/1 | 7. | Number of ECTS credits | 6.00 | ||||||||
| 8. | Lecturer | Dr Vesna Ojleska Latkoska | |||||||||||
| 9. | Course Prerequisites | ||||||||||||
| 10. | 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. |
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| 11. | 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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| 12. | Learning methods:
Slide presentations, interactive lectures, exercises (use of equipment and software), teamwork, case studies, invited guest lecturers, independent preparation and defense of project and seminar work, learning in digital environment (forums, consultations). |
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| 13. | Total number of course hours | 180 | |||||||||||
| 14. | Distribution of course hours | 3 + 3 | |||||||||||
| 15. | Forms of teaching | 15.1 | Lectures-theoretical teaching | 45 hours | |||||||||
| 15.2 | Exercises (laboratory, practice classes), seminars, teamwork | 45 hours | |||||||||||
| 16. | Other course activities | 16.1 | Projects, seminar papers | 30 hours | |||||||||
| 16.2 | Individual tasks | 30 hours | |||||||||||
| 16.3 | Homework and self-learning | 30 hours | |||||||||||
| 17. | Grading | ||||||||||||
| 17.1 | Exams | 0 points | |||||||||||
| 17.2 | Seminar work/project (presentation: written and oral) | 50 points | |||||||||||
| 17.3. | Activity and participation | 0 points | |||||||||||
| 17.4. | Final exam | 50 points | |||||||||||
| 18. | Grading criteria (points) | up to 50 points | 5 (five) (F) | ||||||||||
| from 51 to 60 points | 6 (six) (E) | ||||||||||||
| from 61 to 70 points | 7 (seven) (D) | ||||||||||||
| from 71 to 80 points | 8 (eight) (C) | ||||||||||||
| from 81 to 90 points | 9 (nine) (B) | ||||||||||||
| from 91 to 100 points | 10 (ten) (A) | ||||||||||||
| 19. | Conditions for acquiring teacher’s signature and for taking final exam | Successfully completed project assignment. | |||||||||||
| 20. | Forms of assessment |
The students are obliged to complete and present a project assignment during the semester. A final written and/or oral exam is scheduled during the exam sessions. The students complete the course if they pass the final exam and had previously completed and presented the project assignment during the semester. The final grade takes into account the points from both the final exam and the project assignment. |
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| 21. | Language | Macedonian and English | |||||||||||
| 22. | Method of monitoring of teaching quality | Self-evaluation | |||||||||||
| 23. |
Literature |
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| 23.1. | Required Literature | ||||||||||||
| No. | Author | Title | Publisher | Year | |||||||||
| 1. | R. C. Eberhart, and Y. Shi | Computational Intelligence: Concepts to Implementations | Morgan Kaufmann | 2011 | |||||||||
| 2. | Andries P. Engelbrecht | Computational Intelligence: An Introduction, 2nd Edition | John Wiley | 2007 | |||||||||
| 3. | James M. Keller, Derong Liu, and David B. Fogel | Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation | Wiley-IEEE Press | 2016 | |||||||||
| 23.2. | Additional Literature | ||||||||||||
| No. | Author | Title | Publisher | Year | |||||||||
| 1. | Jyh-Shing Roger Jang, Chuen-Tsai Sun, Eiji Mizutani | Neuro-Fuzzy and Soft Computing (A Computational Approach to Learning and Machine Intelligence) | Prentice Hall | 1997 | |||||||||
| 2. | Robert E. King | Computational Intelligence in Control Engineering | CRC Press | 1999 | |||||||||
| 3. | Witold Pedrycz | Computational Intelligence: An Introduction | CRC Press | 1997 | |||||||||
