Machine Learning Methods in Non-Life Insurance
Course content
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.
MSc Programme in Actuarial Mathematics
MSc Programme in Mathematics-Economics
MSc Programme in Statistics
Knowledge:
- 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
Skills:
A general ability to use machine learning methods to solve practical problems
Competences:
- Know how to use R to solve practical problems
4 hours of lectures per week for 7 weeks
Lecture notes
Non-life insurance 2 (Skade 2) or similar. A class in regression
is very useful. It is possible to follow the class without these,
but of course it will be more demanding.
Academic qualifications equivalent to a BSc degree is
recommended.
- ECTS
- 7,5 ECTS
- Type of assessment
-
Written assignment, 12 timer12 hour take-home exam. Collaboration not allowed.
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- External censorship
One external examiner.
Criteria for exam assessment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.
Single subject courses (day)
- Category
- Hours
- Lectures
- 28
- Preparation
- 124
- Project work
- 42
- Exam
- 12
- English
- 206
Kursusinformation
- Language
- English
- Course number
- NMAK17005U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 block
- Placement
- Block 3
- Schedulegroup
-
B
- Capacity
- No limit.
The number of seats may be reduced in the late registration period - Studyboard
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Contracting faculty
- Faculty of Science
Course Coordinator
- Jostein Paulsen (7-73787c7d6e727749766a7d7137747e376d74)
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Courseinformation of students