Computer-Intensive Statistics and Data Mining
Syllabus, Master's level, 1MS009
This course has been discontinued.
- Code
- 1MS009
- 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, 22 April 2016
- Responsible department
- Department of Mathematics
Entry requirements
120 credits with Analysis of Regression or equivalent.
Learning outcomes
In order to pass the course (grade 3) the student should be able to
- give an account for the theoretical foundation of Markov Chain Monte Carlo-methods and to use such techniques to solve given statistical problems;
- give an account for the principles behind random number generators;
- use simulation methods such as Bootstrap and SIMEX;
- use EM methods;
- use non-parametric statistical models;
- use statistical software, preferably R.
Content
The purpose of the course is to give the student a good overview about statistical techniques that have been developed during the last years due to increasing computer capacity. Resampling techniques, Jack-knife, bootstrap. . 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.
Instruction
Lectures, problem solving sessions and computer-assisted laboratory work.
Assessment
Written examination (8 credit points) at the end of the course as well as assignments (2 credit points) in accordance with instructions at course start.
Reading list
- Reading list valid from Autumn 2022
- Reading list valid from Autumn 2019
- Reading list valid from Spring 2019
- Reading list valid from Autumn 2016
- Reading list valid from Autumn 2013
- Reading list valid from Spring 2010, version 2
- Reading list valid from Spring 2010, version 1
- Reading list valid from Autumn 2008
- Reading list valid from Autumn 2007