Course - Artificial Intelligence and Machine Learning for Information Security Applications - IMT6171
IMT6171 - Artificial Intelligence and Machine Learning for Information Security Applications
About
Examination arrangement
Examination arrangement: Portfolio from project work
Grade: Passed / Not Passed
| Evaluation | Weighting | Duration | Grade deviation | Examination aids |
|---|---|---|---|---|
| Portfolio from project work | 100/100 | ALLE |
Course content
- Artificial Neural Networks
- Deep Learning
- Agent based Simulations
Learning outcome
Having completed the course, the candidate should have:
-Knowledge: The candidate is in the forefront of knowledge within the fields of artificial intelligence and machine learning in the field of information security. The candidate can evaluate the design and implementation of information security research and development projects using AI and ML algorithms. The candidate has the ability to discuss and explain the usefulness and weakness of various algorithms in his\her PhD project.
-Skills: The candidate can select, formulate, implement and test AI and ML algorithms as part of PhD project.
-General competence: The candidate has the ability to communicate and lead discussions on recent research about AI and ML. The candidate has the ability to evaluate and critique mechanisms for AI and ML.
Learning methods and activities
-Reading papers in the field of research with connections to AI and ML
-Seminars with presentations of progress
-Review of AI and ML algorithms and writing and presenting a report on the applicability of the algorithms for own research
-Selecting an algorithm and applying and testing the algorithm in own research
-Delivering a paper that can be submitted to a conference or a journal at the intersection of the field of own research and AI\ML, having selected, implemented and tested the algorithm.
Further on evaluation
Re-sit: None
Forms of assessment: In this course, the candidates are expected to develop a solution for the use of AI\ML for their own research. The assessment is based on the portfolio of work they produce while researching and solving the use of AI\ML for their own research. The candidate is expected to write an intermediary report and a final research paper on the work. The candidates must provide a presentation of results and findings in the final seminar while they present progress in earlier seminars. All parts of the assessment must be passed to pass the course.
Specific conditions
Admission to a programme of study is required:
Information Security and Communication Technology (PHISCT)
Required previous knowledge
Fundamental programming and algorithms
Course materials
Papers related to AI and ML
The course book is "Introduction to Artificial Intelligence" by Wolfang Ertel, Second Edition, Springer.
No
Version: 1
Credits:
5.0 SP
Study level: Doctoral degree level
Term no.: 1
Teaching semester: SPRING 2025
Language of instruction: English
Location: Gjøvik
- Information Security
- Informatics
Department with academic responsibility
Department of Information Security and Communication Technology
Examination
Examination arrangement: Portfolio from project work
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
- Spring ORD Portfolio from project work 100/100 ALLE
-
Room Building Number of candidates
- * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"