Machine Learning

Објавено: June 28, 2022
1. Course Title Machine Learning
2. Code 4ФЕИТ01З007
3. Study program КСИАР
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 Gorjan Nadjinski
9. Course Prerequisites Passed: Programming and Аlgorithms
10. Course Goals (acquired competencies): Students will be introduced to the basic machine learning principles, concepts, and techniques. Upon successful completion of the course, students will be able to independently solve practical engineering tasks using machine learning algorithms and approaches.
11. Course Syllabus: Introduction to machine learning. Supervised learning: Single-variable and multi-variable linear regression. Gradient descent method. Polynomial regression. Logistic regression. Classification as a machine learning problem. Regularization. Neural networks. Support vector machines. Unsupervised learning. k-means clustering. Feature compression. Anomaly detection systems. Machine learning for large data sets. Examples of machine learning algorithms implementation for real problem solving.
12. Learning methods: Combined: presentations, homework, project assignments, practical laboratory work.
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 30
16.2. Individual tasks 30
16.3. Homework and self-learning 45
17. Grading 17.1. Exams 0
17.2. Seminar work/project (presentation: written and oral) 40
17.3. Activity and participation 0
17.4. Final exam 60
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 Completion of the laboratory work assignments.
20. Forms of assessment

Two partial written exams are envisaged during the semester (at the middle and the end of the semester, each with a duration of 120 minutes), as well as a mandatory project that the students are supposed to finish and present during the semester.
1. The students who have passed the partial exams and have successfully finished and presented the project, are considered to have passed the course. The presentation of the project is with duration not longer than 60 minutes. The final grade is formed based on the points from the partial exams and the points obtained from the project.
2. In the planned exam sessions a final written exam is taken (duration 120 minutes). The students who have passed the final written exam, and have finished and presented the mandatory project previously during the semester, are considered to have passed the course. The final grade is formed based on the points from the exams, and the points acquired from the project.
21. Language Macedonian and English
22. Method of monitoring of teaching quality Internal evaluation and polls
23. Literature
23.1. Required Literature
No. Author Title Publisher Year
1 S. Marsland Machine Learning: An Algorithmic Approach CRC Press 2015
2 M. Kuhn, K. Johnson Applied Predictive Modeling Springer 2016
23.2. Additional Literature
No. Author Title Publisher Year
1 S. Raschka, V. Mirjalili Python Machine Learning Packt Publishing 2017
2 C. Bishop Pattern Recognition and Machine Learning Springer 2006