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 |