IKT441 ICT Seminar 2 (Autumn 2022)
- ECTS Credits:
- 7.5
- Responsible department:
- Faculty of Engineering and Science
- Course Leader:
- Linga Reddy Cenkeramaddi
- Lecture Semester:
- Spring
- Teaching language:
- English
Table of contents
The course is connected to the following study programs
- Artificial Intelligence and The Internet of Things, Master's Programme
- Artificial Intelligence, 5-year master programme
Teaching language
Learning outcomes
- be able to design, analyze, and implement advanced systems, or solve an advanced problem.
- have comprehensive knowledge 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.
Contents
The course is based upon ongoing research in the department or relevant needs of the industry, and deepens expertise 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 the students have chosen. The students may be required to present scientific papers.
Pre-approved course topics:
- Certifications
- eHealth
- Reinforcement learning
- Embedded Systems
- Intelligent Radio Frequency Sensing
- 5G and IoT: Advanced
Be aware that all topics in the seminar courses might not 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.
Teaching and learning methods
Combination of lectures, assignments, paper studies, lab, report writing and self-study. The tasks are done individually or in small groups with group supervision.
The work load for the average student is approximately 200 hours.