Project in Software Development in Image Analysis and Machine Learning

Education facts

Code: 1MD036

The course consists of 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 a 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, and 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 six 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 you should develop the ability to participate constructively in dialogue on ethical issues and motivate your choices. At a project level, we can imagine multidisciplinary teams in collaboration with other programs, which in a suitable way combine skills.

Entry requirements

120 credits including 40 credits in mathematics and 60 credits in computer science, including Statistical Machine Learning, a second course in computer programming, Introduction to Image Analysis or Computer-Assisted Image Analysis I and Data Ethics and Law. Participation in Deep Learning and Advanced Deep Learning for Image Analysis, or participation in Deep Machine Learning for Image Analysis. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Project in Software Development in Image Analysis and Machine Learning

  Uppsala University

  Uppsala

  Start date: 31 August 2026

  End date: 17 January 2027

  Pace of study: 50 %