Reinforcement Learning

1.

Subject title

Reinforcement Learning

Учење со поттикнување

2.

Code

m23_w_124

3.

Study program

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

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 Кире Триводалиев проф. д-р Соња Гиевска

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

The purpose of the subject is to acquire the knowledge of modeling agent systems in which the learning ability of agents acting in undetermined environments is through learning by encouraging. The student will be capable of modeling and simulating agent systems intended for weeklying environments using deep learning by encouraging.

11.

Subject content:

Abstraction of agents. Agents intended for non -deserted environments. Markovi Portions of Decision -making. Learning by encouraging. Deep learning by encouraging. Applying learning by encouraging real -world problems (eg games, robots, optimization, natural languages ??processing). Games theory as a mathematical model for modeling and simulation of negotiations, cooperation, competition and agent collapse. Modeling and simulation of multi-agent systems and strategies for coordination, dealing with insecurity in knowledge and judgment. Computational evolutionary biology.

12.

Learning methods:

Предавања поддржани со презентации преку слајдови, интерактивни предавања, вежби (користење на опрема и софтверски пакети), тимска работа, студија на случај, 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 до 15.2, и 16.1 до 16.3

20.

Language of instruction

Македонски и англиски

21.

Quality assurance method

Механизми на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature