Entry requirements

120 credits including 90 credits mathematics, Participation in either Regression analysis, Introduction to data science or Financial derivatives. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Learning outcomes

The purpose of the course is to give the student an overview on statistical techniques based on increased computer capacity.

On completion of the course the student shall be able to:

• give an account for the principles behind random number generators;
• explain the principles of simulation based on Monte Carlo;
• apply methods of computer intensive estimation, such as bootstrap;
• use Expectation Maximization (EM) algorithms;
• apply methods of Monte Carlo 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 like Kernel estimation. Expectation Maximization algorithms. Applications within data science/finance/statistics. The course contains an applied project with a specialization within one of the following areas: Financial mathematics, data science or statistics.

Instruction

Lectures and labs.

Assessment

Home assignments (5 credits) and project work which is presented orally (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

The course cannot be included into the same degree as any of the courses Computer intensive statistics and data mining DS (1MS043), Computer intensive statistics and data mining (1MS009) or Monte Carlo Methods with Financial Applications (1MA214).