Master's Programme in Information and Communication Technology
Artificial Intelligence, 5-year master programme
Language of instruction
On successful completion of this course, the student should
be able to design, analyze, and implement basic systems, or solve basic problems selected from a range of ongoing research topics in ICT (or a project defined by an external company).
The students should also have comprehensive knowledge of and the ability to work out functional solutions within their chosen fields of study.
The course is based on ongoing research in the department or in relevant industry. The course will deepen the expertise of a student within a focused application or research area. Individually or in small groups, students specialize in topics that are approved by members of the academic staff. The topics should be relevant for the specialization profile that the students have chosen. The students may be required to present scientific papers.
Pre-approved course topics: - Artificial intelligence and learning systems (including one lecture introducing its application in eHealth). This topic is a compulsory basis for all students who want to proceed with the eHealth direction in ICT Seminar 2 (IKT441-G) and ICT Seminar 3 (IKT442-G) or 4 (IKT446-G). - Cisco Certification Associate CCNA - Embedded Systems - 5G and IoT: Fundamentals
Be aware that not all topics in the seminar courses might be available every semester.
In addition to the pre-approved course topics, it is possible to propose a certain topic that a student can work on from research groups at UiA or from industry. The course responsible person must approve the topic based on two requirements: (1) The topic should be within the ICT domain and the technical depth of the topic is at Master level. (2) There is an employee at UiA with at least a Master's degree in the relevant field who is the supervisor of the topic.
The course is organized in combination of lectures, assignments, paper studies, labs, report writing and self-study. The tasks are done individually or in small groups with group supervision.
The workload for the average student is approximately 200 hours.
Assessment methods and criteria
Graded portfolio assessment, individually or in groups. Groups are given joint grades. Information about the content of the portfolio will be given in Canvas at the start of the semester.
The study programme manager, in consultation with the student representative, decides the method of evaluation and whether the courses will have a midterm- or end of term evaluation, see also the Quality System, section 4.1. Information about evaluation method for the course will be posted on Canvas.