NMAK22019U Machine Learning Methods in Non-Life Insurance
Volume 2022/2023
Education
MSc Programme in Actuarial Mathematics
MSc Programme in Mathematics-Economics
MSc Programme in Statistics
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.
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
Literature
Lecture notes
Recommended Academic Qualifications
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.
Academic qualifications equivalent to a BSc degree is recommended.
Teaching and learning methods
4 hours of lectures per week
for 7 weeks
Workload
- Category
- Hours
- Lectures
- 28
- Preparation
- 124
- Project work
- 42
- Exam
- 12
- Total
- 206
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Exam
- Credit
- 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.
- Aid
- Without aids
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
- Re-exam
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 information
- Language
- English
- Course code
- NMAK22019U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 3
- Schedule
- B
- Course capacity
- No limit.
The number of seats may be reduced in the late registration period
Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Contracting faculty
- Faculty of Science
Course Coordinators
- Munir Hiabu (2-75704875697c7036737d366c73)
Saved on the
07-04-2022