Mathematics

This course builds up a toolbox of numerical optimization methods for building solutions in future studies, thereby making it an ideal supplement for students from many different fields in science. The course is taught both at a theoretical level that goes into deriving the math, and also on an implementation level with focus on computer science and good programming practice. Students participate in weekly programming exercises where they implement the algorithms and methods introduced from theory, and apply their own implementations to case-study problems like computing the motion of a robot hand or fitting a model to highly non-linear data. Topics include: first order optimality conditions, Karush-Kuhn-Tucker conditions, Taylors theorem, mean value theorem, nonlinear equation solving, linear search methods, trust region methods, linear least-squares fitting, regression problems, and normal equations.

The implementation of sound quantitative actuarial models is a vital task to assess risk in insurance, finance, and other industries and professions. This course provides a self-contained introduction to both theoretical and practical implementation of various quantitative modelling techniques applicable to finance and insurance. The course combines diverse quantitative disciplines, from probability to statistics, from actuarial science to quantitative finance. Students are able to apply the acquired knowledge to evaluate various insurance products.