NMAK17005U Machine Learning Methods in Non-Life Insurance
MSc Programme in Actuarial Mathematics
MSc Programme in Mathematics-Economics
MSc Programme in Statistics
Basic theory of penalized linear regression, splines, additive models, neural networks, multivariate adaptive splines, projection pursuit regression, regression trees, random forests, boosting. Also various methods of classification.
- Standard penalized methods such as ridge regression and the lasso
- Know splines, additive, additive models, neural networks, MARS
- Regression trees, random forest, boosting
- Classification with classical methods as well as machine learning 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
- Written assignment, 12 timer12 hour take-home exam. Collaboration not allowed.
- Exam registration requirements
Two mandatory assignments must be approved and valid before the student is allowed attending the exam.
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- External censorship
One external examiner.
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.