Advanced machine learning

1.

Subject title

Advanced machine learning

Напредно машинско учење

2.

Code

IS-Z-04

3.

Study program

Inteligent Systems, Cloud Computing, IT management, Bioinformatics, Data science in computer science and engineering, Security, Cryptography and Coding, Еducation with ICT, Eco-informatics, Internet Technologies and cyber security, Computer Science, Software for embedded systems, Software Engineering, Cloud Computing, IT management, Bioinformatics, Security, Cryptography and Coding, Software Engineering, Statistics and Data Analytics, 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)

Втор циклус

6.

Academic year / semester

5 / Зимски

7. Number of ECTS credits

6.0

8.

Instructor

проф. д-р Георгина Мирчева ворн. проф. д-р Мирослав Мирчев проф. д-р Соња Гиевска

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

The student will be capable of using advanced algorithms and techniques in the field of machine learning.

11.

Subject content:

This is an open subject where the candidate can choose things on a project related to the latest achievements in the field of machine learning (MU). Possible topics include the following areas: medicine, biosignation processing, natural language processing (understanding of texts, machine translation and translation assisted by machine, statistical processing of natural languages ??and more); His theoretical (new trends in MA`s theory); Data engineering for his models (selection and cleaning data, selection of attributes (Feature Engineering, Data Standardization), Deep Learning (neuroscience and convolution neuronal networks, tensorflow); Advanced themes that include: graphics models, kernel methods , Boosting, bagging, semi-reviewed and active learning, and a tensor approach to data analysis.

12.

Learning methods:

Предавања поддржани со презентации преку слајдови, 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, учење во електронско опкружување (форуми, консултации).

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

60 + 0 + 45 + 45 + 30 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

60 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

0 hours

16.

Other forms of activities

16.1.

Project tasks

45 hours

16.2.

Independent tasks

45 hours

16.3.

Homework

30 hours

17.

Grading method

17.1.

Tests

15 points

17.2.

Seminar work / project (presentation: written and oral)

45 points

17.3.

Activities and learning

15 points

17.4.

Final exam

0 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 активности 15.1 и 15.2

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