Machine learning

1.

Subject title

Machine learning

Машинско учење

2.

Code

F23L3S036

3.

Study program

Стручни студии за програмирање, Примена на информациски технологии, Софтверско инженерство и информациски системи, Компјутерско инженерство, Интернет, мрежи и безбедност, Информатичка едукација, Software engineering and information systems, Примена на информациски технологии, Софтверско инженерство и информациски системи, Компјутерско инженерство, Интернет, мрежи и безбедност, Software engineering and information systems, Компјутерски науки, Стручни студии за програмирање, Компјутерски науки, Bioinformatics, Statistics and Data Analytics,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Прв циклус

6.

Academic year / semester

3 / Летен

7. Number of ECTS credits

6.0

8.

Instructor

ворн. проф. д-р Александра Дединец ворн. проф. д-р Мирослав Мирчев

9.

Prerequisites for enrollment

Веројатност и статистика или Бизнис статистика или Математика 3

10.

Subject goals and competencies:

The aim of the course is for the students to become familiar with the basics of modern machine learning techniques. Upon completion of the course, candidates will have deeper knowledge of advanced technologies and methods of machine learning; they will be able to understand, analyze and formulate general problems in the field of machine learning; they can successfully apply algorithms for machine learning in solving real problems; will be able to conceptualize, analyze, realize and evaluate the performance of a machine learning system.

11.

Subject content:

Introduction to Machine Learning, Generative Models, Gaussian Models, Univariate and Multivariate Linear Regression, Logistic Regression (Hypothesis Representation, Cost Functions, Error Evaluation, Model Selection and Validation), Unsupervised Learning, Mixed Models and EM Algorithm, Kernel methods, support vector machines, Neural networks, regularization in neural networks, Classification and regression decision trees, Deep learning.

12.

Learning methods:

Предавања со користење на презентации, интерактивни предавања, вежби (користење на опрема и софтверски пакети), тимска работа, пример случаи, поканети гости предавачи, самостојна изработка и одбрана на проектна задача и семинарска работа.

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

30 + 45 + 15 + 15 + 75 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

30 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

45 hours

16.

Other forms of activities

16.1.

Project tasks

15 hours

16.2.

Independent tasks

15 hours

16.3.

Homework

75 hours

17.

Grading method

17.1.

Tests

10 points

17.2.

Seminar work / project (presentation: written and oral)

15 points

17.3.

Activities and learning

10 points

17.4.

Final exam

70 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.

Condition for signature and taking final exam

Реализирани актибвности 15.2 и 16.1

20.

Language of instruction

Македонски и англиски

21.

Quality assurance method

механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature