- Han, J., Kamber, M., & Pei, J. (2022). Data mining: Concepts and techniques (4th edition), Morgan Kaufmann.
- Russell, S. J. & Norvig, P. (2020). Artificial intelligence: A modern approach (4th edition), Pearson.
Reference literature
- Harrison, G. (2016). Next Generation Databases: NoSQLand Big Data. Apress.
- Relevant scientific articles
EduSinglePage
Course offerd as part of course packege
Course, master’s level
15 credits
Malmö | daytime | 100%
15 January 2029 - 25 March 2029
Course code: CD642E
This course is offered as part of coursepackage:
Course content
The aim of the course is for the student to develop knowledge in the application of artificial intelligence (AI), data processing methods and data storage systems related to IoT applications.
The course contains the following components:
- Key concepts in AI
- Application of logic and logic programming for 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
- CD120A Algorithms and Data Structures (7.5 credits)
- CM152A Mathematical Statistics (7.5 credits)
- 11 credits from the course CD640E Introduction to IoT (15 credits)
Course literature
Course evaluation
Malmö University provides students who participate in, or who have completed a course, with the opportunity to express their opinions and describe their experiences of the course by completing a course evaluation administered by the University. The University will compile and summarise the results of course evaluations. The University will also inform participants of the results and any decisions relating to measures taken in response to the course evaluations. The results will be made available to the students (HF 1:14).
