Intelligent Information Systems

Објавено: June 28, 2022
1. Course Title Intelligent Information Systems
2. Code 4ФЕИТ07З004
3. Study program КТИ
4. Organizer of the study program (unit, institute, department) Faculty of Electrical Engineering and Information Technologies
5. Degree (first, second, third cycle) First cycle
6. Academic year/semester IV/7 7. Number of ECTS credits 6
8. Lecturer D-r Hristijan Gjoreski
9. Course Prerequisites Passed: Data structures and algorithm analysis, Data structures and programming, Programming and algorithms
10. Course Goals (acquired competencies): Understanding the concept of intelligence in modern information and communication systems. With this course, the student will gain knowledge about the practical use of tools and techniques for modeling intelligent systems, decision making, agents and multi-agent systems, deep learning. You will also gain theoretical and practical knowledge about using different types of data when building intelligent systems: tabular data, image, sound, text, working with natural languages.
11. Course Syllabus: Natural and Artificial Intelligence: historical definition of intelligence and overview of the development of intelligent systems. Intelligent systems modeling, knowledge representation, learning methods. Methods and algorithms from Artificial Intelligence applied to creating intelligent systems: data acquisition, data processing, and modelling with Machine Learning and Deep Learning; Agent and multi-agent systems, reinforcement learning. Analysis of various data types, including: structured and unstructured data, time series, images, sound, etc. Algorithms for analysis of natural languages (text, speech – Natural Language Processing) and chatbots.
Implement algorithms and models using the Java and Python.
12. Learning methods: Lectures, auditory and laboratory exercises, independent learning, independent work on project tasks and preparation of seminar papers
13. Total number of course hours 2 + 2 + 1 + 0
14. Distribution of course hours 180
15. Forms of teaching 15.1. Lectures-theoretical teaching 30
15.2. Exercises (laboratory, practice classes), seminars, teamwork 45
16. Other course activities 16.1. Projects, seminar papers 0
16.2. Individual tasks 45
16.3. Homework and self-learning 60
17. Grading 17.1. Exams 0
17.2. Seminar work/project (presentation: written and oral) 0
17.3. Activity and participation 10
17.4. Final exam 90
18. Grading criteria (points) up to 50 points 5 (five) (F)
from 51to 60 points 6 (six) (E)
from 61to 70 points 7 (seven) (D)
from 71to 80 points 8 (eight) (C)
from 81to 90 points 9 (nine) (B)
from 91to 100 points 10 (ten) (A)
19. Conditions for acquiring teacher’s signature and for taking final exam Practical (laboratory) exercises and finished project
20. Forms of assessment Two partial exams during the semester lasting 120 minutes each or one final written exam in an appropriate exam session lasting 120 minutes. Evaluation of laboratory exercises. Possibility to do a seminar project that will be evaluated.
21. Language Macedonian and English
22. Method of monitoring of teaching quality Internal evaluation and surveys.
23. Literature
23.1. Additional Literature
No. Author Title Publisher Year
1 Stuart Russell, Peter Norvig Artificial Intelligence: A Modern Approach Pearson; 4th edition (April 28, 2020) 2020
2 Aurélien Géron Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems O’Reilly Media; 2nd edition (October 15, 2019) 2019