NMAK17005U  Machine Learning Methods in Non-Life Insurance

Volume 2017/2018
Education

MSc Programme in Actuarial Mathematics

Content

Basic theory of penalized linear regression, additive models, generalized additive models, some machine learning regression methods, Cox regression and regression trees.

Learning Outcome

Knowledge:

  • Standard penalized methods such as ridge regression and the lasso
  • Know splines, additive and generalized additive models.


Skills:

  • Some machine learning regression methods such as projection pursuit regression, neural networks, MARS and boosting.
  • Know the basics of Cox regression
  • Know about different regression tree models such as CART, random forest and how to boos a regression tree.


Competences:

  • Know how to use R to solve practical problems

See Absalon for a list of course literature.

Non-life insurance 2 (Skade 2) or similar.
4 hours of lectures per week for 7 weeks
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 handed in at the latest two weeks before the beginning of the re-exam week. They must be approved before the re-exam.

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
  • Preparation
  • 135
  • Lectures
  • 28
  • Project work
  • 42
  • Total
  • 206