Machine Learning Methods in Non-Life Insurance
Course content
- 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.
Education
MSc Programme in Actuarial Mathematics
MSc Programme in Mathematics-Economics
MSc Programme in Statistics
Learning outcome
Knowledge:
- 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
Skills:
A general ability to use machine learning methods to solve practical problems
Competences:
- Know how to use R to solve practical problems
Teaching and learning methods
4 hours of lectures per week for 7 weeks
Literature
Lecture notes
Recommended prerequisites
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.
Feedback form
Collective
Exam
- ECTS
- 7,5 ECTS
- Type of assessment
-
Oral examination under invigilation
- Type of assessment details
- 30min oral examination (without preparation time).
- Aid
- Without aids
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
Criteria for exam assessment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.
Course type
Single subject courses (day)
Workload
- Category
- Hours
- Lectures
- 28
- Preparation
- 124
- Project work
- 42
- Exam
- 12
- English
- 206
Kursusinformation
- Language
- English
- Course number
- NMAK22019U
- 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
- Munir Hiabu (2-736e4673677a6e34717b346a71)
Saved on the
07-04-2022
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Courseinformation of students
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