Efteruddannelsesudvalget vil gerne reklamere for, at der i foråret 2018 kører to nyudviklede aktuarkurser på KU.
Machine Learning Methods in Non-Life Insurance
* Standard penalized methods such as ridge regression and the lasso
* Know splines, additive and generalized additive models.
* 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.
* 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
At the end of the course the student is expected to have:
* Knowledge about estimation, valuation, and pricing in the Markov life insurance setup.
* 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.
* 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