Computer-Intensive Statistics and Applications
Syllabus, Master's level, 1MS049
- Code
- 1MS049
- Education cycle
- Second cycle
- Main field(s) of study and in-depth level
- Computer Science A1N, Data Science A1N, Financial Mathematics A1N, 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, 10 October 2023
- Responsible department
- Department of Mathematics
Entry requirements
120 credits including 60 credits mathematics and/or data science with at least 45 credits mathematics. Participation in one of the courses 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).