Automated machine learning

1.

Subject title

Automated machine learning

Автоматизирање на процеси во машинско учење

2.

Code

F23L3S163

3.

Study program

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

проф. д-р Билјана Тојтовска Рибарски доц. д-р Бојан Илијоски ворн. проф. д-р Панче Рибарски

9.

Prerequisites for enrollment

Алгоритми и податочни структури или Примена на алгоритми и податочни структури

10.

Subject goals and competencies:

Introduce students with the basic steps to deploy machine learning models in production, optimization of pipelines in ML, full lifecycle design of ML models, CI/CD in ML, managing ML code, monitoring models in production, managing models.

11.

Subject content:

1. ETL 2. Data pipelines, Streaming pipelines 3. Automating ML - managing code 4. Automating ML - managing models 5. Automating ML - managing processes 6. Model logging 7. Model tracking 8. Model serving 9. CI/CD 10. Testing 11. Capstone project

12.

Learning methods:

Предавања, аудиториски вежби, лабораториски вежби, проектни задачи, домашни задачи, разработка на програмски пакети за автоматизација на процеси во машинско учење

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

40 points

17.2.

Seminar work / project (presentation: written and oral)

15 points

17.3.

Activities and learning

20 points

17.4.

Final exam

100 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

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