Entry requirements

120 credits including 40 credits in mathematics and 60 credits in computer science, including Statistical Machine Learning, several variable calculus, a second course in computer programming, Introduction to Image Analysis or Computer-Assisted Image Analysis I and Data Ethics and Law. Participation in Deep Machine Learning for Image Analysis. 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:

• account for in-depth methodological knowledge in image analysis and machine learning;
• integrate theory and methodological knowledge for image analysis and machine learning, and to use, compare and evaluate different models in realistic problem situations;
• plan and execute a project in the main area of image analysis and machine learning in a group within a given time frame;
• analyse a given problem, identify sub-problems and find and choose solution methods for these;
• independently search, evaluate and use scientific and technical information to achieve project objectives and meet the requirements specifications;
• demonstrate a solution to a project task and demonstrate whether it meets the requirement specifications;
• in writing as well as orally present the project assignment, prerequisite, argue for the chosen methodology and present the results.

Content

The course consists of a group work on a project from outside academia or research, as well as a number of lectures on topics related to the development of software products. Examples of topics for such lectures are: Framework for software development (for example, agile methods such as Scrum), project and time planning, IP and licensing issues, business plan and market analysis, quality systems, rules and regulations, oral and written presentation, ethics. The lectures are intertwined with the introduction of the projects, problem analysis and planning. The scope of the projects is suitable for groups of approximately 6 students. After the introductory phase, the students complete the projects, and at the end of the course this is presented to the course participants and external project owners. Ethical considerations are integrated into the projects, with the goal that students should develop the ability to participate constructively in dialogue on ethical issues and motivate their choices. At a project level, we can imagine multidisciplinary teams in collaboration with other programs, which in a suitable way combine skills.

Instruction

Lectures, project work.

Assessment

Written report and oral presentations at the seminars.

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.