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Insurance Analytics

  • Teacher(s):   M.Bladt  
  • Course given in: English
  • ECTS Credits: 3 credits
  • Schedule: Autumn Semester 2019-2020, 2.0h. course (weekly average)
  •  sessions
  • site web du cours course website
  • Related programme: Master of Science (MSc) in Actuarial Science

 

Objectives

The goal is to provide a solid understanding of some of the most common machine learning methods, and to be able to apply and interpret them on insurance data.

Contents

  • Linear models: regression, general linear models, generalized linear models
  • Mixed Models
  • Regularization: Lasso, Ridge regression, Elastic Net
  • Model selection
  • Model Asessment
  • Estimation Methods
  • Generalized Additive Models
  • Regression Trees
  • Random Forests
  • Gradient Boosting
  • Additional topics (Neural Networks, Ensemble Learning, and others)

References

Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning (Vol. 1, No. 10). New York: Springer series in statistics.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, p. 18). New York: springer.

Evaluation

First attempt

Exam:
Oral 0h25 minutes
Documentation:
Not allowed
Calculator:
Not allowed
Evaluation:

Students will prepare the analysis of a dataset. Examination will be based on a presentation of a topic of the course, followed by general questions and on their analysis as well.

Retake

Exam:
Oral 0h25 minutes
Documentation:
Not allowed
Calculator:
Not allowed
Evaluation:


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