Programme description for Artificial Intelligence and The Internet of Things, Master's Programme (2025–2027)
Facts about the program
- ECTS credits:
- 120
- Study duration:
- 2 years
- Organisation:
- Full-time
- Campus:
- Campus Grimstad
- Teaching language:
- English
- Study level:
- Master's degree (2 years)
Table of contents
Study Model
Autumn 2025
Master ICT - Study Profile
AI - compulsory courses
AI - compulsory courses
IoT - compulsory courses
IoT - compulsory courses
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Spring 2026
AI - compulsory courses
AI - compulsory courses
AI - elective courses 2. semester
IoT - compulsory courses
IoT - compulsory courses
IoT - elective courses 2. semester
- IKT433 Distributed and Big Data Systems 7.5 stp
- IKT449 Selected Security Topics 7.5 stp
- IKT452 Computer Vision 7.5 stp
- IKT453 Intelligent Data Management 7.5 stp
- IKT460 Reinforcement Learning 7.5 stp
- IKT462 Digital Health: Fundamentals 7.5 stp
- IKT468 Applied Algorithms 7.5 stp
- IKT518 Security Protocols 7.5 stp
- IKT521 Cryptography 7.5 stp
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Autumn 2026
AI - compulsory courses
AI - compulsory courses
AI - elective courses 3. semester
- IKT433 Distributed and Big Data Systems 7.5 stp
- IKT436 Advanced Internet Services and Protocols 7.5 stp
- IKT454 5G and IoT: Fundamentals 7.5 stp
- IKT467 Languages for Modeling and Simulation 7.5 stp
- IKT524 Digital Health: Advanced 7.5 stp
- IKT526 Emerging AI technologies 7.5 stp
- MAS416 Modelling and Simulation of Mechatronic Systems 7.5 stp
- MM-403 Interaction Design 7.5 stp
- PRA400 Internship for Master’s Students in Engineering and Science 15 stp
- SV-420 UiA Co-creation - OpenLab 10 stp
Elective Courses (non-STEM) (7,5 credits)
Elective Courses (non-STEM) (7,5 credits)
IoT - compulsory courses
IoT - compulsory courses
IoT - elective courses 3. semester
- IKT433 Distributed and Big Data Systems 7.5 stp
- IKT457 Tsetlin Machines 7.5 stp
- IKT467 Languages for Modeling and Simulation 7.5 stp
- IKT469 Deep Neural Networks 7.5 stp
- IKT522 Software Security 7.5 stp
- IKT524 Digital Health: Advanced 7.5 stp
- MAS416 Modelling and Simulation of Mechatronic Systems 7.5 stp
- PRA400 Internship for Master’s Students in Engineering and Science 15 stp
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Spring 2027
AI - compulsory courses
AI - compulsory courses
AI - elective courses 4. semester
IoT - compulsory courses
IoT - compulsory courses
IoT - elective courses 4. semester
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Autumn 2027
AI - elective courses 5. semester
IoT - compulsory courses
IoT - elective courses 5. semester
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Spring 2028
AI - compulsory courses
AI - elective courses 6. semester
IoT - compulsory courses
IoT - elective courses 6. semester
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Autumn 2028
AI - compulsory courses
IoT - compulsory courses
Choose specialisation to view courses
Spring 2029
AI - compulsory courses
IoT - compulsory courses
Choose specialisation to view courses
Learning outcomes
Knowledge
- have obtained advanced knowledge of ICT and specialized knowledge within their selected specialization profile
- have a thorough knowledge of the main scientific methods used in ICT research
- be able to analyze ICT problems, both from a research and an industrial perspective
Skills
- Be able to apply their knowledge in new areas within ICT.
- Be able to analyze ICT problems, both from a research perspective and from an industrial perspective within their specialization profiles.
- Be able to analyze existing theories and methods, and solve applied and theoretical problems independently.
- Be able to apply relevant research and development methods in an independent manner.
- Be able to critically analyze various information sources, and apply these for structuring and formulating arguments.
- Be able to conduct an independent research and development project under supervision, in agreement with the prevailing academic and ethical standards for research.
- Be able to contribute to the innovation and the creation of new ideas in academic and industrial ICT projects.
General competence
- be able to apply their knowledge in new areas in order to pursue advanced tasks and projects
- be able to communicate comprehensive independent work by mastering the relevant forms of expression used in ICT research and development
- be able to disseminate their knowledge of applied and theoretical solutions to certain problems, orally and in writing, to their peers and to a general audience
Admission requirements
The admission requirement is a completed and passed three-year bachelor’s degree in engineering within the areas Computer Science, Electronics, Renewable Energy, or Mechatronics, in line with the National Curriculum Regulations for Engineering Education in Norway. Applicants with background from renewable energy, must have passed courses in basic object-oriented programming and control engineering. Applicants with background in Mechatronics must have passed courses in basic object-oriented programming and embedded computer systems.
Furthermore, the following requirements are set, which in part go beyond the minimum requirements in the national curriculum regulations:
• At least 25 credits in relevant mathematics
• At least 5 credits in statistics
• At least 7.5 credits in physics
The weighted average grade of C or better is required.
Admission takes place in accordance with regulations on admission to studies and courses at the University of Agder https://lovdata.no/dokument/SF/forskrift/2021-11-24-3370?q=uia
This program is subject to export control regulations. Export control applies to applicants from countries where there is suspicion of the development and use of weapons of mass destruction. UiA conducts a special assessment of these applicants.
Recommended previous knowledge
General description and profile of the programme
The master’s programme is organized into two specializations:
- Artificial Intelligence - emphasizes various technical aspects of artificial intelligence, as well as its application in current domains.
- The Internet of Things - focuses on modern communication technology, security, development, and the use of services.
Additionally, you will learn about project implementation, research methods, teamwork, and group dynamics. As a student in the master’s program in AI and IoT, you actively participate in seminars and projects that address new and current topics. Strong connections with the industry enable many students to work on master's theses based on relevant issues from external clients.
Teaching and learning methods
A range of different teaching and working methods will be used, including lectures, Internet discussion groups, seminars, group work and exercises, individual computer exercises, laboratory exercises, case studies and participation in ongoing research projects. In many courses, students are expected to submit well-structured project reports, usually written in a standardized document template. Every semester the students must prepare and update individual education plans. Students are expected to take full responsibility for their own learning and personal progress in keeping with their individual education plans. It is assumed that the students will attain parts of the curriculum objectives through self-study, especially in the seminar courses. Students are also required to have a laptop computer with a wireless network interface. There is usually no compulsory attendance at ordinary lectures, but students may be subject to attendance requirements for some courses, or parts of a course. This will be stated in the course description for the different courses.
Regarding master's thesis: For every student/group there will be 10 compulsory supervision meetings for the regular Master's Thesis, and 20 compulsory supervision meetings for the full-year Master's Thesis.
Assessment methods
Internationalisation
Information and communication technology is an area where companies and professionals both cooperate and compete internationally as well as nationally. The literature in the programme is in English, and English is the language of teaching. Written assignments and project reports may be submitted in English, Norwegian or another Scandinavian language. In several courses, Norwegian and international students work together in groups. This will prepare the students for an international working life and teach them to communicate about their field of expertise in an international environment.
The programme provides the students an opportunity to take parts of the programme at partner universities abroad. Full-time Master students can take the 3rd and/or 4th semester at a partner university abroad. In order to become an exchange student, the courses/thesis work that the student is planning to undertake at the foreign institution must be given a preliminary approval by the study programme manager at UiA, and a written Student Contract must be signed with the International Education Office. The Faculty of Engineering and Science has a large number of partner universities with which the student exchange can be arranged in an efficient manner. The master´s programme is open to incoming exchange students from foreign universities, who may follow one or two semesters of the programme.
Requirements for continuing in the study programme
Before starting the 30 ECTS Master's Thesis (IKT590-G), the student must successfully complete courses amounting to at least 75 ECTS credits. For the 60 ECTS Master’s Thesis (IKT591-G) the requirement is 60 ECTS credits. All courses that lead directly to the thesis work must be successfully completed.
A prerequisite for students to take the full year Master's Thesis, i.e. IKT591-G, is a minimum grade point average of B in the 1st and 2nd semester. In addition, this choice must be approved by the study programme manager.
Occupational profiles of graduates and access to further studies
Qualification awarded
Student evaluations
Fees
Contact person
Other information
A compulsory course in Health, Safety and Environment is recommended for students admitted to the programme.
A wireless network is available on campus and the master students use their own laptop computers in most courses.
It is a requirement that all students at UiA have their own portable computer (laptop) for use in teaching and examinations, see Examination Regulations, University of Agder, §12d