About the course

The purpose of the course is that the student acquires the basic methods and techniques in the field of artificial intelligence and autonomous systems, with particular emphasis on practical use in the development of software for data science problems.

Course content

The course includes the following elements:

- Recommendation systems: user- and content-based recommendations, recommendation algorithms (such as neighborhood-based, collaborative filtering and matrix factorisation), context-aware recommendations, cold start, eliciting/implicit ratings, evaluation and metrics.
- Information retrieval, knowledge acquisition, knowledge representation and reasoning, the semantic web, constructing and querying knowledge graphs, extracting data from online sources and source alignment
- Probabilistic models and decision theory, decision making under uncertainty, optimisation, dynamic programming, methods for adversarial and heuristic search
- Practical methods for data mining
- Machine learning for both supervised and unsupervised learning. Algorithms for classification, prediction, and clustering.

Syllabus and course literature

You can find a list of literature in the syllabus, along with other details about the course.

Entry requirements and selection

Entry requirements

Bachelor of Science in computer science or related subjects.

Knowledge equivalent to English 6 at Swedish upper secondary level.

At least 15 credits in programming.

At least 7.5 credits in mathematics.


University credits completed 100%

Course evaluation

The University provides students who are taking or have completed a course with the opportunity to share their experiences of and opinions about the course in the form of a course evaluation that is arranged by the University. The University compiles the course evaluations and notifies the results and any decisions regarding actions brought about by the course evaluations. The results shall be kept available for the students. (HF 1:14).


For more information about the education: