Advanced topics in artificial intelligence

Advanced topics in artificial intelligence

1.

Subject title

Advanced topics in artificial intelligence

Напредни теми од вештачката интелигенција

2.

Code

IS-Z-02

3.

Study program

Inteligent Systems, Cloud Computing, Data science in computer science and engineering, IT management, Bioinformatics, Security, Cryptography and Coding, Еducation with ICT, Eco-informatics, Internet Technologies and cyber security, Computer Science, Software Engineering, Cloud Computing, IT management, Bioinformatics, Security, Cryptography and Coding, Software Engineering, Software for embedded systems, Statistics and Data Analytics, Statistics and Data Analytics,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Втор циклус

6.

Academic year / semester

5 / Зимски

7. Number of ECTS credits

6.0

8.

Instructor

проф. д-р Ана Мадевска Богданова проф. д-р Андреа Кулаков

9.

Prerequisites for enrollment

10.

Subject goals and competencies:


The student will be capable of using advanced algorithms and techniques in the field of artificial intelligence and machine learning.

11.

Subject content:


The subject of advanced artificial intelligence topics offers an open approach, the cadet will be able to choose to work on a project related to the latest achievements in the field of artificial intelligence (VI). Possible topics cover the following areas: metaphorical judgment and analogy; theoretical you (new trends in the theory of you; and legal judgment, ethics of you, interpretable you); Cibora theory;

12.

Learning methods:


Предавања поддржани со презентации преку слајдови, интерактивни предавања, вежби (користење на опрема и софтверски пакети), тимска работа, пример случаи, поканети гости предавачи, самостојна изработка и одбрана на проектна задача и семинарска работа, учење во електронско опкружување (форуми, консултации).

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

60 + 0 + 45 + 45 + 30 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

60 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

0 hours

16.

Other forms of activities

16.1.

Project tasks

45 hours

16.2.

Independent tasks

45 hours

16.3.

Homework

30 hours

17.

Grading method

17.1.

Tests

15 points

17.2.

Seminar work / project (presentation: written and oral)

45 points

17.3.

Activities and learning

15 points

17.4.

Final exam

0 points

18.

Grading criteria (points / grade)

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.

Condition for signature and taking final exam

реализирани активности 15.1 и 15.2

20.

Language of instruction

македонски и англиски

21.

Quality assurance method

механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

No.

Author

Title

Publisher

Year

7566

Stuart Russell, Peter Norvig

Artificial Intelligence: A Modern Approach, 3rd Edition

Prentice Hall

2010

7567

Edited by Sven Dickinson

IEEE Transactions on Pattern Analysis and Machine Intelligence

Institute of Electrical and Electronics Engineers

1979

7568

Edited by Haibo He

IEEE Transactions on Neural Networks and Learning Systems

IEEE Computational Intelligence Society

2012

7569

Ajay Thampi

Interpretable AI

Manning publishing

2021

22.2.

Additional literature

No.

Author

Title

Publisher

Year