Design of AI systems
About
This course is for students who are interested in the empirical methods applied to the field of software engineering. The course introduces quantitative and qualitative methods in software engineering with accompanying statistical methods used for analysis.
The course contains:
- Descriptive and inferential statistical methods applied to software engineering.
- Conducting qualitative and quantitative methods in software engineering.
- Methods for analyzing quantitative and qualitative data in software engineering.
- Usage of statistical tools.
Prerequisites and selection
Entry requirements
The student must have successfully completed courses in:
- 7,5 hec mathematical thinking (DIT025 or DIT856 or equivalent) or a course in basic mathematics (containing e.g. calculus, linear algebra, discrete mathematics)
- 7,5 hec mathematical statistics (e.g. MSG810 or DIT862 or similar)
- 7,5 hec programming in a general-purpose language (e.g. Python/Java/C or similar),
- An introductory course in Data Science and/or AI, for examPle DIT852 or DIT405 or equivalent.
We strongly recommend to also take a course in Machine learning, for example DIT866or similar, or that such a course is taken in parallel alongside this course.
Selection
Selection is based upon the number of credits from previous university studies, maximum 285 credits
