| 1. | Course Title | Artificial Intelligence and Deep Learning | |||||||||||
| 2. | Code | 4ФЕИТ07004 | |||||||||||
| 3. | Study program | 20-IMSA, 21-PNMI | |||||||||||
| 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 Hristijan Gjoreski | |||||||||||
| 9. | Course Prerequisites | ||||||||||||
| 10. | Course Goals (acquired competencies):
Understanding the concept of artificial intelligence and deep learning. 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:
Studying the concept of Artificial Intelligence and Deep Learning. Intelligent systems modeling, knowledge representation, learning methods. Methods and algorithms from Artificial Intelligenced and modelling with Machine Learning and Deep 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 | 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 | Regular attendance on lectures | |||||||||||
| 20. | Forms of assessment | Project assignment and final exam. | |||||||||||
| 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. | Stuart Russell, Peter Norvig | Artificial Intelligence: A Modern Approach | Pearson; 4th edition (April 28, 2020) | 2020 | |||||||||
| 23.2. | Additional Literature | ||||||||||||
| No. | Author | Title | Publisher | Year | |||||||||
| 1. | 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 | |||||||||
