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ФИНКИ

Subject

Decision support systems

1. Course Title Decision support systems
Decision support systems
2. Code F23L3W156
3. Study Programme —
4. Organizer of the study programme (unit, institute, department or division) Faculty of Computer Science and Engineering
5. Degree level (first, second, third cycle) First Cycle
6. Academic year / semester 7 / Winter
7. Number of ECTS credits 6
8. Teacher Георгина Мирчева
9. Prerequisites for enrolling in the course Artificial Intelligence or Introduction to Data Science or Machine Learning
10. Objectives of the course programme (competences) This course is an introduction to the application of data analysis for making business decisions. The goal of the course is for students to become familiar with decision support methods, techniques, and systems, as well as decision analysis. They will also learn about knowledge acquisition and knowledge representation techniques. Upon completion of the course, students will gain the knowledge to use decision support systems, correctly select an appropriate decision support system in a given business context, as well as to design, develop, and manage decision support systems.
11. Course content Lectures:
1. Вовед во експертни системи
2. Дрва за одлучување
3. Баесови класификатори
4. Пронаоѓање на документи
5. Системи за препораки
6. Невронски мрежи
7. Онтологии
8. Семантички веб
9. Ризици. Полезност. Системи на знаење.
10. Предвидување на временски серии
11. Системи за поддршка во одлучувањето
12. Матна логика

Practical Classes:
1. Вовед во Python
2. Вовед во Python
3. Дрва за одлучување
4. Дрва за одлучување
5. Баесови класификатори
6. Пронаоѓање на документи
7. Системи за препораки
8. Системи за препораки
9. Невронски мрежи
10. Невронски мрежи
11. Предвидување на временски серии
12. Предвидување на временски серии
12. Learning methods lectures, classroom exercises, laboratory exercises, project assignments, homework
13. Total available time 6 ECTS x 30 hours = 180 hours
14. Distribution of available time 30 + 45 + 15 + 15 + 75 = 180 hours
15. Forms of teaching activities
15.1. Lectures - theoretical instruction 30 hours
15.2. Exercises (laboratory, auditory), seminars, teamwork 45 hours
16. Other forms of activities
16.1. Project assignments 15 hours
16.2. Independent assignments 15 hours
16.3. Home study 75 hours
17. Assessment method
17.1. Tests 0 points
17.2. Seminar paper / project (presentation: written and oral) 15 points
17.3. Activities and learning 10 points
17.4. Final exam 30 points
18. Grading criteria (points / grade)
up to 50 points5 (five) (F)
from 51 to 60 points6 (six) (E)
from 61 to 70 points7 (seven) (D)
from 71 to 80 points8 (eight) (C)
from 81 to 90 points9 (nine) (B)
from 91 to 100 points10 (ten) (A)
19. Requirement for obtaining a signature and taking the final exam Completed activities 15, 16
20. Language of instruction Macedonian and English
21. Method for monitoring the quality of teaching internal evaluation and survey mechanism
22. Literature
22.1. Required literature
1. R. Sharda, D. Delen, E. Turban | Business Intelligence, A Managerial Perspective on Analytics, 3rd edition | Pearson | 2013
2. Vicki L. Sauter | Decision Support Systems for Business Intelligence, 2nd edition | John Wiley & Sons | 2012
3. George M. Marakas | Decision Support Systems, 2nd edition | Prentice Hall | 2002
4. Efraim Turban, Jay E. Aronson, Ting-Peng Liang, and Ramesh Sharda | Decision Support and Business Intelligence Systems, 9th edition | Prentice Hall | 2011
5. Daniel J. Power | Decision Support Systems: Concepts and Resources for Managers | Greenwood Publishing Group | 2002
22.2. Additional literature
No. Author Title Publisher Year
Деканат Dean’s Office Студии Studies Акти Acts Проекти Projects