The course is connected to the following study programs

Teaching language

English

Course contents

The course is based upon ongoing research in the department or relevant requirements of industry. The course will deepen the competence and 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 to the specialization profile that the students have chosen. The student may be required to present scientific papers.

The student can select only one of the pre-approved topics/direction and pursue that for the semester. A successful participation in a topic/direction within this course might depend on the participation in corresponding seminars in previous semesters. Additional topics / directions are subject of negotiation between the student, a topic expert from the academic staff, and the study programme manager.

Pre-approved course topics / directions include:

(1) eHealth

Overview of this direction

The students will deepen their competence, understanding and practical experience in the application of technologies like sensor-based data collection / IoT, data representation / ontologies, machine learning, natural language processing (NLP), and more, for intelligent eHealth applications and services. Examples are clinical decision support systems and personalized wellness coaching.

Pre-requisites

As the target project(s) will depend to a high degree on Artificial Intelligence (AI) technologies, students should have participated the "Artificial Intelligence - Learning Systems" direction in IKT440-G (ICT Seminar 1).

Furthermore, the project builds on top of eHealth specific competence lectured in the "eHealth topic/direction" in IKT441-G (ICT Seminar 2).

(2) Database Management

The focus of this direction is on distributed and unstructured database management, as well as on core relationship between data warehousing, information integration, and information fusion technologies.

(3) Embedded Systems

A selection of project topics from the area of "Embedded Systems" will be announced at the beginning of the semester in the LMS (CANVAS). The project requires a working implementation of the system to be built, tested, and demonstrated.

(4) Cisco Certification (CCNA/CCNP or other relevant certification)

This direction has the same procedure as Cisco Certification Associate CCNA in ICT Seminar 1 (IKT440-G).

(5) Selected Topics in Beyond 5G and IoT Networks

Together with the tutor, a student who selects this direction is expected to identify a topic which is targeted at advancing the state-of-the-art techniques beyond 5G and IoT. Depending on the interest and expertise of the student, the topic could be either theoretical or practical, or a combination of them. The potential topics will very likely be relevant to the ongoing research activities at the Dept. of ICT, UiA.

(6) Knowledge engineering and representation

The focus is on knowledge representation together with some focus on architecture, querying and reasoning.

Be aware that not all topics in the seminar courses might be available each 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 a permanent employee who is the supervisor of the topic.

Learning outcomes

On successful completion of this course, the student should

  • be able to design, analyze, and implement advanced systems, or solve an advanced problem.

  • have comprehensive knowledge of and the ability to work out solutions within the sub area the student chooses to specialize in.

  • be able to identify relevant literature and apply theoretical models to the problems at hand.

Teaching methods

Combination of lectures, assignments, paper studies, lab, and report writing. The tasks are done individually or in small groups with group supervision.

The workload for the average student is approximately 200 hours.

Evaluation

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.

Offered as Single Standing Module

Yes. Subject to availability or capacity.

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 by the start of the course.

Last updated from FS (Common Student System) June 30, 2024 1:36:17 AM