Expected learning outcomes
- Understanding of cutting-edge problems in computational and forensic sciences as well as their applications specific domains, as for example threat intelligence, automation of malware analysis, biometric identification, network intrusion detection, internet investigation, deep-package mining and multimedia-content analysis in forensics.
- The students can use relevant scientific methods in independent research and development in computational forensics.
- The students are capable of carrying out an independent limited research or development project in computational forensics under supervision, following the applicable ethical rules.
The students can work independently and are familiar with computational forensic terminology.
Deepening of knowledge and skills in computer-assisted digital investigations and forensics using specific methods to realistic case scenarios. Methods may include yet are not limited to:
- Forensic Statistics
- Forensics Data Science
- Pattern Recognition
- Machine Learning
- Predictive Analytics
- Information Retrieval
- Data Mining
- Signal and Video Processing
- Computer Visualization
A selection of possible case scenarios will be made available at the beginning of the course.
Net Support Learning
PBL (Problem Based Learning)
Form(s) of Assessment
Form(s) of Assessment (additional text)
The students have to deliver an essay and can deliver a project.
In case a student decides to deliver both the essay and the project, then both will count for 50% towards the final grade and both parts have to be passed to pass the course.
In case the student only delivers an essay then the final grade will be based 100% on the grade of the essay
Alphabetical Scale, A(best) – F (fail)
Internal examiner. An external examiner will be used every 5th year, next time in 2020.
The whole course must be repeated the next time the course is running.
Scientific Articles related to the field of Specialization.
Replacement course for
Computational Forensics - IMT4641 - 5 ECTS