Syllabus for Computer-Intensive Statistics and Data Mining DS

Learning outcomes

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.

On completion of the course, the student should be able to:

• give an account for the principles behind random number generators;
• give an account for the principle for simulation using Monte Carlo methods;
• apply methods for computer-intensive estimation methods, such as bootstrap;
• use Expectation Maximization (EM)-algorithms;-s
• apply the Monte Carlo methodology to solve real-world problems;

Content

Random number generators and simulation of random objects. Monte Carlo methods, including Markov Chain Monte Carlo and quasi-Monte Carlo methods. Estimation techniques such as for example Kernel estimators. Expectation Maximization-algorithms. Applications. The course contains an applied project part with specialization within a defined area within data analysis.

 

Instruction

Lectures and computer-assisted laboratory work.

Assessment

Written assignments (5 credits) and project work which is presented orally (2.5 credits).

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university..

Other directives

This course cannot be included in the same degree as the course ​Computer-Intensive Statistics and Data Mining 10 credits or Computer-Intensive statistics with applications 10 credits.