Machine Learning Forensics
1. |
Subject title |
Machine Learning Forensics Форензика со машинско учење |
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2. |
Code |
m23_s_075 |
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3. |
Study program |
Cloud Computing, IT management, Bioinformatics, Security, Cryptography and Coding, Еducation with ICT, Eco-informatics, Inteligent Systems, Computer Science, Statistics and Data Analytics, Software Engineering, Cloud Computing, IT management, Bioinformatics, Security, Cryptography and Coding, Statistics and Data Analytics, Software Engineering, Software for embedded systems, Data science in computer science and engineering, Internet Technologies and cyber security, |
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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 |
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9. |
Prerequisites for enrollment |
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10. |
Subject goals and competencies: The aim of the course is to: • present the basic concepts of forensics with machine learning • analyze current machine learning forensics technologies • analyze methodologies for applying machine learning to data relevant in the field of forensics
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11. |
Subject content: 1. Introduction 2. Fundamentals of forensics 3. Fundamentals of Machine and Deep Learning 4. Forensics using machine learning on textual data (including social media data) (2 weeks) 5. Forensics using machine learning of images (2 weeks) 6. Forensics using machine learning on audio and video data (2 weeks) 7. Other applications of machine learning in forensics |
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12. |
Learning methods: NULL |
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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 + 15 + 30 + 50 + 40 = 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 |
15 hours |
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16. |
Other forms of activities |
16.1. |
Project tasks |
50 hours
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16.2. |
Independent tasks |
30 hours |
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16.3. |
Homework |
40 hours |
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17. |
Grading method |
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17.1. |
Tests |
45 points |
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17.2. |
Seminar work / project (presentation: written and oral) |
50 points |
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17.3. |
Activities and learning |
10 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 |
NULL |
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21. |
Quality assurance method |
NULL
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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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