NMAK22019U Machine Learning Methods in Non-Life Insurance
MSc Programme in Actuarial Mathematics
MSc Programme in Mathematics-Economics
MSc Programme in Statistics
- Introduction of various machine learning methods. Topics may include but are not limited to: theory of penalized linear regression, splines, additive models, neural networks, multivariate adaptive splines, projection pursuit regression, regression trees, random forests, boosting.
- Discussoion on interpretability. Various topics on interpretable machine learning, including global model-agnostic methods like Partial Dependence Plots (PDP) and Accumulated Local Effects (ALE) plots as well as local model-agnostic methods like Local Interpretable Model-agnostic Explanation (LIME) and SHapley Additive exPlanations (SHAP) values.
- Regression with classical (penalized) methods as well as machine learning methods
- Classification with classical methods as well as machine learning methods
- Various machine learning interpretation methods
A general ability to use machine learning methods to solve practical problems
- Know how to use R to solve practical problems
Academic qualifications equivalent to a BSc degree is recommended.
- Project work
- 7,5 ECTS
- Type of assessment
- Oral examination under invigilation
- Type of assessment details
- 30min oral examination (without preparation time).
- Exam registration requirements
Two mandatory assignments must be approved and valid before the student is allowed attending the exam.
- Without aids
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
Same as ordinary exam. If the the two mandatory homework assignments were not approved before the ordinary exam they must be resubmitted. They must be approved three weeks before the beginning of the re-exam week.
Criteria for exam assesment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.
- Course code
- 7,5 ECTS
- Full Degree Master
- 1 block
- Block 3
- Course capacity
- No limit.
The number of seats may be reduced in the late registration period
- Study Board of Mathematics and Computer Science
- Department of Mathematical Sciences
- Faculty of Science
- Munir Hiabu (2-75704875697c7036737d366c73)