Machine Learning with Economic and Financial Applications
Syllabus, Master's level, 2NE825
- Code
- 2NE825
- Education cycle
- Second cycle
- Main field(s) of study and in-depth level
- Economics A1F
- Grading system
- Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very good (B), Excellent (A)
- Finalised by
- The Department Board, 14 May 2024
- Responsible department
- Department of Economics
Entry requirements
Bachelor's degree, equivalent to a Swedish Kandidatexamen, from an internationally recognised university. Also required is 60 credits in economics and 15 credits in statistics (or a closely related subject).
Learning outcomes
This course aims to introduce students to data analysis, focusing on machine learning and prediction models with economic and finance applications. At the end of the course, students should be familiar with the foundations of machine learning and the most common prediction models and be able to use these methods to analyze large datasets. The course will also introduce causal machine learning. An additional learning outcome is that students will be able to use the statistical software language R to implement these methods.
Content
The course will use lectures and computer lab sessions.
Assessment
Written exam