| 1. | Course Title | Cognitive Computing in ICT | |||||||||||
| 2. | Code | 4ФЕИТ11002 | |||||||||||
| 3. | Study program | 7-NKS, 20-IMSA, 21-PNMI | |||||||||||
| 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 Valentin Rakovikj | |||||||||||
| 9. | Course Prerequisites | ||||||||||||
| 10. | Course Goals (acquired competencies):
Getting acquainted with the characteristics and concepts of cognitive computing. Relevant aspects of cognitive computing for ICT scenarios. Understanding the concept of machine learning and beyond big data. Analysis and design of ICT services based on cognitive computing. Elements of cognitive computing and communications in the cloud. Ability to develop cognitive services and applications, for SDN, NFV, Cloud/Open RAN. |
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| 11. | Course Syllabus:
Introduction. Basic concepts. Possible system architectures. Basic connection between ICT and cognitive computing. Aspects of cognitive computing based on neural networks in ICT. Cognitive analysis in ICT. Aspects and basics concepts of cognitive computation in IoT and IoE. Machine Learning in SDN, NFV. ML-based orchestration and deployment for virtualized network resources and elements. Artificial Intelligence and Machine Learning in RAN architectures. Concepts, interfaces and solutions for AI/ML in next generation and B5G system such as O-RAN. |
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| 12. | Learning methods:
Lectures, independent work on project tasks and preparation of seminar work |
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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 | 30 points | |||||||||||
| 17.2 | Seminar work/project (presentation: written and oral) | 50 points | |||||||||||
| 17.3. | Activity and participation | 20 points | |||||||||||
| 17.4. | Final exam | 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 | Regular attendance at classes | |||||||||||
| 20. | Forms of assessment | One full exam with a duration of max 120 minutes in a corresponding exam session and presentation of seminar work. | |||||||||||
| 21. | Language | Macedonian and English | |||||||||||
| 22. | Method of monitoring of teaching quality | Self-evaluation | |||||||||||
| 23. | Literature | ||||||||||||
| 23.1. | Required Literature | ||||||||||||
| No. | Author | Title | Publisher | Year | |||||||||
| 1. | Vijay V. Raghavan, et al. | Handbook of Statistics: Cognitive computing, theory and applications | North Holland | 2016 | |||||||||
| 2. | Kai Hwang, Min Chen | Big-Data Analytics for Cloud, IoT and Cognitive Computing | Wiley | 2018 | |||||||||
