Master’s studies

Syllabus for Computer-Intensive Statistics and Data Mining

Datorintensiv statistik och informationsutvinning

A later update of this course syllabus has been published.


  • 10 credits
  • Course code: 1MS009
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Mathematics A1N
  • Grading system: Fail (U), 3, 4, 5.
  • Established: 2007-03-15
  • Established by: The Faculty Board of Science and Technology
  • Revised: 2008-11-03
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: week 44, 2008
  • Entry requirements: 120 credit points including Analysis of Regression and Variance or corresponding course
  • Responsible department: Department of Mathematics

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.

    Reading list

    Reading lists

    Applies from: week 04, 2010

    • Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome The elements of statistical learning : data mining, inference, and prediction

      2. ed.: New York: Springer, 2009

      Find in the library