NMAK17005U  Machine Learning Methods in Non-Life Insurance

Volume 2019/2020
Education

MSc Programme in Actuarial Mathematics

MSc Programme in Mathematics-Economics

MSc Programme in Statistics

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.

 

Learning Outcome

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

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 then.

Academic qualifications equivalent to a BSc degree is recommended.
4 hours of lectures per week for 7 weeks
Collective
Credit
7,5 ECTS
Type of assessment
Oral examination, 30 minutes under invigilation
Half time used to present a randomly chosen topic from a list of questions available before the exam. There will be no 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
External censorship
One external examiner.
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.

  • Category
  • Hours
  • Exam
  • 1
  • Lectures
  • 28
  • Preparation
  • 135
  • Project work
  • 42
  • Total
  • 206