Efteruddannelsesudvalget vil gerne reklamere for, at der i foråret 2018 kører to nyudviklede aktuarkurser på KU. 

 

Machine Learning Methods in Non-Life Insurance

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

 

Underviser: Jostein Paulsen

Yderligere information: http://kurser.ku.dk/course/nmak17005u/2017-2018

 

 

Inference, Market Consistent Valuation and Pricing in Life Insurance

Learning Outcome

At the end of the course the student is expected to have:

* Knowledge:

   * Knowledge about estimation, valuation, and pricing in the Markov life insurance setup.

* Skills:

   * Skills to formulate, formalize, and solve theoretical and practical problems related to estimation, valuation, and pricing in the Markov life insurance setup.

   * Skills to implement procedures related to estimation, valuation, and pricing in the Markov life insurance setup in R.

* Competences:

   * The course will strengthen the student's competences in navigating inside the Markov life insurance setup and develop the student's ability to formulate and handle new models inside this setup.

 

Undervisere: Thomas Møller, Kristian Buchardt, Christian Furrer

Yderligere information: http://kurser.ku.dk/course/nmak17003u/2017-2018 

  

 

 

 

Sponsor

Deloitte KPMG Keylane Edlund Lifecode