Subject

Machine Learning Forensics

Course Title

Machine Learning Forensics
Machine Learning Forensics

Code

m23_s_075

Study Programme

—

Organizer of the study programme (unit, institute, department or division)

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

Second cycle

Academic year / semester

10 / Summer

Number of ECTS credits

Teacher

Alexandra Dedinec

Prerequisites for enrolling in the course

—

10.

Objectives of the course programme (competences)

The goal of the course is to:
• Present the basic concepts of forensics with machine learning
• analyze current forensic technologies using machine learning
Analyze methodologies for applying machine learning to data relevant in the field of forensics.

11.

Course 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 on images (2 weeks)
6. Forensics using machine learning on audio and video data (2 weeks)
7. Other applications of machine learning in forensics

12.

Learning methods

NULL

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

45 + 15 + 30 + 50 + 40 = 180 hours

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	45 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	15 hours

16.

Other forms of activities

16.1.	Project assignments	50 hours
16.2.	Independent assignments	30 hours
16.3.	Home study	40 hours

17.

Assessment method

17.1.	Tests	45 points
17.2.	Seminar paper / project (presentation: written and oral)	50 points
17.3.	Activities and learning	10 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.

Requirement for obtaining a signature and taking the final exam

NULL

20.

Language of instruction

NULL

21.

Method for monitoring the quality of teaching

NULL

22.

Literature

22.1.	Required literature 1. Mohammad Haroon, Manish Madhava Tripathi and Faiyaz Ahmad \| Application of Machine Learning In Forensic Science \| IGI Global \| 2020 2. Nour Moustafa \| Digital Forensics in the Era of Artificial Intelligence \| CRC Press \| 2022
22.2.	Additional literature No. Author Title Publisher Year