- 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.
Reference literature:
- Harrison, G. (2016). Next Generation Databases: NoSQLand Big Data, Apress, 1st ed. Edition
- Relevant scientific articles on the topic of future ethics.
EduSinglePage
Course offered as a part of a programme
Course, master’s level
15 credits
Malmö | daytime | 50%
19 January 2026 - 7 June 2026
Code for the course: DA642E
About the course
The course provides insights into the use of data processing methods based on Artificial Intelligence, and data storage systems related to IoT applications.
This course is offered as part of programme:
Course content
- Key concepts in AI
- Machine learning algorithms, including both supervised and unsupervised learning
- Techniques for data mining
- Autonomous agents and multiagent systems
- Distributed AI solutions for IoT systems
- Management of large amounts of data (big data) and data storage
- Data integration and data quality
- Personal privacy, ethical aspects of AI, such as algorithmic discrimination, impartial and unbiased processing of data, as well as legal aspects including GDPR, the right to be forgotten and data aggregation.
Entry requirements
- Bachelor of Science in Engineering (at least 180 credits) or a bachelor’s degree in computer science or related fields such as computer engineering, computer and information science, software engineering, informatics, telecommunications or electrical engineering.
- At least 15 credits in programming.
- Equivalent of English 6/English B in secondary school.
- Minimum of a passing grade from the course: Introduction to IoT (DA640E)
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).
