Artificial Intelligence

Објавено: June 23, 2023
1. Course Title Artificial Intelligence
2. Code 4ФЕИТ01003
3. Study program 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):

To master the advanced concepts of artificial intelligence and to be able to implement the acquired knowledge in different technical and non technical fields.

11. Course Syllabus:

Introduction: Artificial intelligence and agents, designing agents, agent design space, prototypical applications. Agent architectures and hierarchical control: agents, agent systems, hierarchical control, acting with reasoning. Reasoning with certainty: searching for solutions, reasoning with constraints, propositions and interference. Planning with certainty: representing states, actions and goals, forward planning, regression planning, planning as CSP, partial-order planning. Learning with certainty: supervised machine learning, basic models, overfitting, neural networks and deep learning.

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).

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.

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. David L. Poole, Alan K. Mackworth Artificial Intelligence: Foundations of Computational Agents Cambridge University Press 2017
23.2.       Additional Literature
No. Author Title Publisher Year
1.  Stuart Russell, Peter Norvig  Artificial Intelligence: A Modern Approach  Pearson Education  2015