- Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques (3rd ed.). Waltham: Morgan Kaufmann.
- Russell, Stuart Jonathan & Norvig, Peter (2010). Artificial intelligence: a modern approach. (3rd ed.) Boston: Pearson Education.
- Segaran, Toby. 2007. Programming Collective Intelligence (First ed.). O'Reilly.
EduSinglePage
Course offered as a part of a programme
Course, master’s level
15 credits
Malmö | daytime | 50%
1 September 2025 - 18 January 2026
Code for the course: DA631E
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.
This course is offered as part of programme:
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.
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.
Course literature
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).
