Degree Project: Master of Science (one year) in Computing Science with specialization artificial intelligence, 15 Credits

Contents

The student practices their ability to specify, plan, perform, and present an independent work. Through their work, the student integrates previous  knowledge and immerses themselves in at least one Computing Science topic in Artifical Intelligence. The result of the work is presented in English both orally and in writing.

The course is partitioned into two modules.

Module 1. Independent work (14 credits)
The student performs the degree project as an independent work, either within the scope of a research project at the university or a development project in industry. The work shall concern some form of problem solving and lead to the formation of new knowledge. Therefore, it must not only consist of routine programming. The work must lead to subject specialization in relation to some previously completed course on the advanced level and shall relate to current research. The specialization shall be within a Computing Science topic in Artificial Intelligence and be of relevance to the independent work.

Module 2. Presentation (1 credit)
The student presents their work in writing, in the form of a scientific report, and defends it orally, in the form of a
public seminar. The student will also dispute someone else's work.

Expected learning outcomes

Knowledge and understanding
After completing the course, the student should be able to:

• (FSR 1) demonstrate in-depth knowledge in at least one Computing Science topic in Artificial Intelligence

Competence and skills
After completing the course, the student should be able to:

• (FSR 2) critically and independently identify and formulate a question at issue
• (FSR 3) plan and with adequate methods perform qualified tasks within given time frames
• (FSR 4) critically and systematically integrate knowledge
• (FSR 5) clearly argue for their conclusions, both orally and in writing
• (FSR 6) independently search for information and evaluate the relevance of information relative to the question at issue

Judgement and approach
After completing the course, the student should be able to:

• (FSR 7) evaluate master's theses based on relevant scientific, societal, and ethical aspects

Required Knowledge

To be admitted you must have a Bachelor's Degree and courses at the advanced level corresponding to 30 ECTS-credits out of which at least 15hp must be in Computing Science and include the following five courses (or equivalent):
- Fundamentals of Artificial Intelligence,
- Artificial Intelligence - Methods and Applications,
- Machine learning,
- Responsible Design of Interactive AI-Systems,
- and either Fundations of Logic and Model Theory, or Statistics for Engineers.

Depending on the focus of the Degree Project some extra prerequisites might be needed.

Proficiency in English equivalent to the level required for basic eligibility for higher studies.

Form of instruction

The teaching consists of independent work with individual supervision by a supervisor appointed by the department. The student is responsible for maintaining regular contact with the supervisor and manage their resources. If the work is external, then the external party will also appoint a supervisor.

Examination modes

Examination of Module 1. Independent work
The module encompasses FSR 2, 3, 6. The module uses the grade scale Fail (U), Pass (G). The assessment is carried out through an ongoing examination consisting of a written project plan submitted in connection to the planning of the work and a written project diary created during the project. Through an ongoing assessment of these documents, the examiner decides if the student has reached the module's expected learning outcomes or not.

Examination of Module 2. Presentation
The module encompasses FSR 1, 4, 5, 7. The module uses the grade scale Fail (U), Pass (G), Pass with distinction (VG). The assessment is carried out through a combination of three tests:

• An oral and written critique of someone else's work.
• An oral presentation of the student's own work.
• A written report of the student's own work and the subject specialization.

Grade on the course
On Module 1, the student receives one of the grades Fail (U) or Pass (G). On Module 2, the student receives one of the grades Fail (U), Pass (G), or Pass with distinction (VG). On the course as a whole, the student receives one of the grades Fail (U), Pass (G), or Pass with distinction (VG). The grade on the course as a whole is determined by the grade on Module 2.

Adapted examination
The examiner can decide to deviate from the specified forms of examination. Individual adaptation of the examination shall be considered based on the needs of the student. The examination is adapted within the constraints of the expected learning outcomes. A student that needs adapted examination shall no later than 10 days before the examination request adaptation from the Department of Computing Science. The examiner makes a decision of adapted examination and the student is notified.

Other regulations

A student may request (but not demand) extra supervision resources past the end of the course. The request should be sent to the study director at the Department of Computing Science.

This course may not be included in a degree, in whole or in part, at the same time as another course with similar content. In case of doubt, the student should consult the study counsellor at the Department of Computing Science and/or the programme coordinator for their degree programme.

If the syllabus has expired or the course has been discontinued, a student who at some point registered for the course is guaranteed at least three examinations (including the regular examination) according to this syllabus for a maximum period of two years from the syllabus expiring or the course being discontinued.