The overall goal of the course is that students acquire knowledge and skills in collecting and processing data from a heterogeneous computerised environment including distributed sensor units, clients and servers.
After completing the course, students will be able to:
Knowledge and Understanding
- Describe concepts related to software agents, multi-agent systems, and autonomous and distributed systems
- Describe properties and concepts of distributed and parallel systems.
Skills and Abilities
- Implement software agents with the support of multi-agent frameworks
- Configure, manage and develop applications for computers with limited memory
- Conduct data analysis related to distributed systems
- Apply scientific approaches in the planning, design, implementation and presentation of quantitative studies
Evaluation Ability and Approach
- Argue for selected methods and techniques in artificial intelligence, data science or statistical analysis for problem solving
- Evaluate the results of analysed collected data and suggest improvements
In addition, software agents and distributed systems that are realised in the practical programme development components of the course are treated. In this context, methods and techniques for artificial intelligence (AI) or data analysis are used and implemented by the student.
The course focuses on quantitative methods that are used during the problem-solving process, including planning, data collection, data transformation, data storage, data analysis and presentation.
Forms of Study
The Swedish grades U–VG.Tests (U - VG), laboratory reports (U - G) and assignments (U - VG).
The final grade is determined by an overall weighting of the examination components included in the course.
- Artificial intelligence 7,5 credits and Statistical Analysis 7,5 credits or Data Science och Machine Learning 7,5 credits
- Object-Oriented Design and Problem-Solving, 7.5 credits
- Research Methodology 7.5 credits First Cycle
- Database Systems, 7.5 credits First Cycle