Applied Machine Learning
1. |
Subject title |
Applied Machine Learning Applied Machine Learning |
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2. |
Code |
DS005 |
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3. |
Study program |
Data science in computer science and engineering, Cloud Computing, IT management, Bioinformatics, Security, Cryptography and Coding, Еducation with ICT, Inteligent Systems, Internet Technologies and cyber security, Computer Science, Software for embedded systems, Software Engineering, Cloud Computing, Bioinformatics, Security, Cryptography and Coding, Software Engineering, Eco-informatics, IT management, Statistics and Data Analytics, Statistics and Data Analytics, |
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4. |
Organizer of the study program (unit, institute, department, division) |
Faculty of Information Sciences and Computer Engineering |
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5. |
Study cycle (first, second, third) |
Втор циклус |
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6. |
Academic year / semester 5 / Летен |
7. Number of ECTS credits 6.0 |
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8. |
Instructor |
ворн. проф. д-р Александра Дединец проф. д-р Андреа Кулаков ворн. проф. д-р Мирослав Мирчев проф. д-р Соња Гиевска |
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9. |
Prerequisites for enrollment |
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10. |
Subject goals and competencies: The Applied Machine Learning teaches students some of the core ideas in machine learning and data science that would go from a real-world business problem to a working and deployable AI solution at scale. The primary focus is to build real-world AI solutions using the skills they have learned in the first semester. The focus will be on practical knowledge more than mathematical or theoretical foundations. In the balance between theory and practice, more preference will be given to the practical and applied aspects of Machine Learning.
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11. |
Subject content: MLOps AutoML Parallelization of ML ML in cloud Prescriptive analytics Time series data analysis Intro to Machine Vision Intro to Sound and Speech ML |
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12. |
Learning methods: Презентации, анкети и сл. |
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13. |
Total available time fund |
6.0 ECTS x 30 hours = 180 hours |
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14. |
Time distribution |
45 + 30 + 30 + 30 + 55 = 180 hours
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15. |
Forms of teaching activities |
15.1. |
Lectures - theoretical teaching |
45 hours |
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15.2. |
Exercises (laboratory, classroom), seminars, team work |
30 hours |
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16. |
Other forms of activities |
16.1. |
Project tasks |
30 hours
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16.2. |
Independent tasks |
30 hours |
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16.3. |
Homework |
55 hours |
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17. |
Grading method |
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17.1. |
Tests |
0 points |
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17.2. |
Seminar work / project (presentation: written and oral) |
30 points |
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17.3. |
Activities and learning |
0 points |
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17.4. |
Final exam |
0 points |
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18. |
Grading criteria (points / grade) |
up to 50 points |
5 (five) (F) |
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from 51 to 60 points |
6 (six) (E) |
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from 61 to 70 points |
7 (seven) (D) |
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from 71 to 80 points |
8 (eight) (C) |
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from 81 to 90 points |
9 (nine) (B) |
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from 91 to 100 points |
10 (ten) (A) |
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19. |
Condition for signature and taking final exam |
NULL |
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20. |
Language of instruction |
Англиски |
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21. |
Quality assurance method |
механизам на интерна евалуација и анкети
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22. |
Literature |
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22.1. |
Mandatory literature |
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22.2. |
Additional literature |
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