Computer-Intensive Statistics and Data Mining

10 credits

Syllabus, Master's level, 1MS009

A revised version of the syllabus is available.
Education cycle
Second cycle
Main field(s) of study and in-depth level
Mathematics A1N
Grading system
Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
Finalised by
The Faculty Board of Science and Technology, 6 November 2007
Responsible department
Department of Mathematics

Entry requirements

120 credit points and Analysis of Regression and Variance

Learning outcomes

In order to pass the course (grade 3) the student should

  • have a thorough knowledge about statistical techniques that have been developed during the last decades due to increasing computer capacity;
  • understand the theoretical foundation of Markov Chain Monte Carlo methods and be able to use such techniques;
  • understand the principles behind random number generators;
  • be able to use simulation methods such as Bootstrap and SIMEX;
  • be able to use computer-intensive non-linear statistical methods;
  • be familiar with EM methods;
  • be able to use non-parametric statistical models;
  • have some experience of applications from image analysis and financial mathematics;
  • be able to use statistical software, preferably R.
  • Content

    Resampling techniques, Jack-knife, bootstrap. Non-linear statistical methods. EM algorithms. SIMEX methodology. Markov Chain Monte Carlo (MCMC) methods. Random number generators. Smoothing techniques. Kernel estimators, nearest neighbour estimators, orthogonal and local polynomial estimators, wavelet estimators. Splines. Choice of bandwidth and other smoothing parameters. Applications. Use of statistical software.


    Lectures, problem solving sessions and computer-assisted laboratory work.


    Written and, possibly, oral examination (4 credit points) at the end of the course. Assignments and laboratory work (6 credit points) during the course.