Degree Project E in Image Analysis and Machine Learning

30 credits

Syllabus, Master's level, 1MD038

Education cycle
Second cycle
Main field(s) of study and in-depth level
Image Analysis and Machine Learning A2E
Grading system
Fail (U), Pass (G)
Finalised by
The Faculty Board of Science and Technology, 6 October 2022
Responsible department
Department of Information Technology

Entry requirements

A Bachelor's degree and, in addition participation in at least 45 credits in image analysis and machine learning at Master's level, including Project in Software Development in Image Analysis and Machine Learning (15 credits) and additional 7.5 credits in one of the possible specialisations in image analysis and machine learning. The following courses shall be completed: Introduction to Image Analysis or Computer-Assisted Image Analysis I, Statistical Machine Learning, Deep Learning for Image Analysis, Data, Ethics and Law. 7.5 credits within one of the possible specializations in image analysis and machine learning completed. The course can only be taken following courses required at Master's level for the degree project. Admission requires a project plan accepted by the department. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Learning outcomes

On completion of the course, the student shall be able to:

  • show a deep knowledge within the chosen field of image analysis and machine learning;
  • in a critical way delimit a scientific problem, plan a scientific study, identify subproblems, choose appropriate methods, carry out the study, interpret and evaluate the results and, if applicable, generate falsifiable a hypotheses to explain the observations all within given time frames;
  • demonstrate skills required to participate in research and development or to work independently in other advanced contexts, utilizing competence from the subject areas of machine learning and image analysis;
  • search and in a critical way interpret, evaluate, compile and apply relevant scientific literature
  • present the results in correct language for different target groups both in scientific and in popular form
  • evaulate a solution to a complex image analysis problem, taking into account both techical and societal and ethical aspects; 
  • give constructive criticism on texts within the study field;
  • identify their need of further knowledge and to take responsibility for developing their knowledge.


An independent project is carried out, where the knowledge from earlier completed courses are applied. The project is guided by a supervisor in close connection to ongoing projects in research or development.


The teaching is devised individually dependent on the direction of the project. Supervision is provided individually or in group.


To pass, a passed oral and written presentation of the degree project is required. The written presentation should consist of a scientific report, a popular summary and a summary in English. To pass, the student must also act as opponent on another degree project within image analysis and machine learning.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

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