91̽»¨

The course is an optional course within Information Systems: The Digitalisation of Society, Bachelor's Programme (N1SVP) and Bachelor in Cognitive 91̽»¨ (N1KOG). The course can also be taken as a free standing course.

English 6 / English B, or equivalent and 7.5 credits in programming in a general programming language, or equivalent.

The progress of recent years in AI and machine learning has resulted in that we, today, live with a large number of applications in our everyday lives. What does that mean for the services we use? And how can we design new such applications? Just as one needs to have a basic understanding of the strength of different materials to design buildings, we need an understanding of the material properties of the algorithms behind these applications.

The course is about learning about AI and machine learning as a design material, to be able to use these in design for new digital services. To do this, you will learn basic concepts and techniques in machine learning to be able to understand its material properties, and how they can be used in design. You will also learn various existing tools and how they can be used to apply machine learning in new services. After this, it is discussed how these material properties can be used in the development of new services, and various applications are critically examined. In the course, we discuss existing and new applications of machine learning, as well as ethical issues regarding the use of these techniques.

On successful completion of the course the student will be able to:

Knowledge and understanding

• describe basic types of machine learning and AI;
• account for differences between different types of machine learning;
• describe how data is used to train models;
• describe everyday applications of machine learning;

Competence and skills

• use machine learning in the design of user-friendly services in a user-centered way;
• set requirements for and collect data adapted to even specific design problems in the design of digital services;
• use existing machine learning tools for new applications;

Judgement and approach

• reflect on how data affects what a machine learns;
• reflect on how ML affects modern system design;
• reflect on ethical issues surrounding machine learning and its application;
• critically review applications of AI.

The teaching consists of lectures, seminars, and exercises.

Language of instruction: English

The examination consists of two assignments of 7.5 credits each.

If a student who has been failed twice for the same examination element wishes to change examiner before the next examination session, such a request is to be granted unless there are specific reasons to the contrary (Chapter 6 Section 22 HF).

If a student has received a certificate of disability study support from the University 91̽»¨ with a recommendation of adapted examination and/or adapted forms of assessment, an examiner may decide, if this is consistent with the course’s intended learning outcomes and provided that no unreasonable resources would be needed, to grant the student adapted examination and/or adapted forms of assessment.

If a course has been discontinued or undergone major changes, the student must be offered at least two examination sessions in addition to ordinary examination sessions. These sessions are to be spread over a period of at least one year but no more than two years after the course has been discontinued/changed. The same applies to placement and internship (VFU) except that this is restricted to only one further examination session.

If a student has been notified that they fulfil the requirements for being a student at Riksidrottsuniversitetet (RIU student), to combine elite sports activities with studies, the examiner is entitled to decide on adaptation of examinations if this is done in accordance with the Local rules regarding RIU students at the University 91̽»¨.

The grading scale comprises: Pass (G) and Fail (U). To obtain the grade Pass on the entire course, the grade Pass is required for all parts.

After completing the course, a course evaluation is carried out. The participating students are given the opportunity to participate anonymously. The results are compiled and made available to the students. Any measures due to the price valuation results are reported. The compilation of the course evaluation is also made available to new students at the start of the next course opportunity.