AutoML

1.

Subject title

AutoML

AutoML

2.

Code

m23_w_049

3.

Study program

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

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 / Летен

7. Number of ECTS credits

6.0

8.

Instructor

доц. д-р Илинка Иваноска ворн. проф. д-р Ристе Стојанов виз. вонр. проф. д-р Томе Ефтимов

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

Within this course, students will be introduced to the basic and advanced concepts applied when working with Automl. There is a lot of evidence where algorithms from machine intelligence contribute to top results in predicting different areas such as natural languages ??processing, computer vision. The problem that exists today is how to choose the best algorithm for the selected scenario and what are its best hyper parameters that will lead to the best results. In this course, students will be introduced to techniques: how to choose the best algorithm for a given data set, how to find the best hyper parameters for the selected algorithm, and what statistical analyzes are appropriate and will lead to robust results.

11.

Subject content:

1. The basic concepts of machine intelligence 2. How to choose representative learning data 3. Selection of the best algorithm 4. Selecting the best hyper parameters. 5. Robust Statistical Analysis 6. Examples in stochastic optimization 7. Examples in classification and regression

12.

Learning methods:

Презентации, книги, електронски материјали, решавање на практични проблеми

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

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

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

30 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

15 hours

16.

Other forms of activities

16.1.

Project tasks

45 hours

16.2.

Independent tasks

45 hours

16.3.

Homework

45 hours

17.

Grading method

17.1.

Tests

20 points

17.2.

Seminar work / project (presentation: written and oral)

45 points

17.3.

Activities and learning

0 points

17.4.

Final exam

30 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

редовна посета на предавањата

20.

Language of instruction

Англиски

21.

Quality assurance method

Проектни задачи, тестови, домашни

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature