Subject

Artificial intelligence

Course Title

Artificial intelligence
Artificial Intelligence

Code

F23L2S030

Study Programme

Bioinformatics, Software Engineering and Information Systems, Computer Science, Software Engineering and Information Systems

Organizer of the study programme (unit, institute, department or division)

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

First Cycle

Academic year / semester

4 / Summer

Number of ECTS credits

Teacher

Andrea Kulakov, Georgina Mircheva, Ilinka Ivanoska, Kire Trivodaliiev, Petre Lameski, Sonya Gievska

Prerequisites for enrolling in the course

A minimum of 36 ECTS credits earned

10.

Objectives of the course programme (competences)

The successful student will have in-depth knowledge of the fundamental areas of artificial intelligence, including search, problem-solving, knowledge representation, reasoning, decision-making, planning, and learning, and their applications. They will also be able to design and implement the key problems of intelligent systems of moderate complexity and to evaluate their behavior.

11.

Course content

Lectures:
1. About Artificial Intelligence
For intelligent agents
2. Introduction to Searching
Uninformed search
3. Informed Search
4. Fulfilling conditions
5. Opposed search
6. Genetic Algorithms
7. Probabilistic reasoning
Bayesian networks
8. Machine Learning Fundamentals
Naive Bayes algorithm
9. Perceptron
10. Decision trees
11. Neural networks
12. Applications of Artificial Intelligence
Natural language processing, machine vision, robotics

12.

Learning methods

Lectures using presentations, interactive lectures, exercises (using equipment and software packages), teamwork, case studies, guest lecturers, independent preparation and defense of a project assignment and a seminar paper.

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

30 + 60 + 15 + 15 + 60 = 180 hours

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	30 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	60 hours

16.

Other forms of activities

16.1.	Project assignments	15 hours
16.2.	Independent assignments	15 hours
16.3.	Home study	60 hours

17.

Assessment method

17.1.	Tests	40 points
17.2.	Seminar paper / project (presentation: written and oral)	15 points
17.3.	Activities and learning	10 points
17.4.	Final exam	50 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.

Requirement for obtaining a signature and taking the final exam

Activities 15.2 and 16.1 completed

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

22.2.

Additional literature

No. Author Title Publisher Year