Course Catalog 2012-2013
International

Basic Pori International Postgraduate Open University

|Degrees|     |Study blocks|     |Courses|    

Course Catalog 2012-2013

MAT-55216 Topics in applied mathematics, 3-5 cr

Additional information

The course topics vary yearly. The aim of the course is to provide advanced-level teaching in a specialised topic of applied mathematics. The teaching may be given as lectures or as a seminar, and often involves guest teachers.
Suitable for postgraduate studies

Person responsible

Seppo Pohjolainen, Mikko Kaasalainen, Esko Turunen, Keijo Ruohonen, Robert Piche, Stephane Foldes, Sirkka-Liisa Eriksson

Lessons

Study type P1 P2 P3 P4 Summer Implementations Lecture times and places
Lectures
Excercises
Assignment



 
 10 h/per
 4 h/per
 15 h/per



 



 



 
MAT-55216 2012-01 Thursday 13 - 16, Td308

Requirements

Course requirements vary with implementation.
Completion parts must belong to the same implementation

Learning outcomes

The student will gain knowledge in a specialised research area of applied mathematics and an understanding of current research questions. The student will be able to understand and solve problems in new situations by applying acquired knowledge, facts, techniques and rules given in the teaching material.

Content

Content Core content Complementary knowledge Specialist knowledge
1. Core content is specified separately for each implementation.   Small research project management, report writing   

Prerequisite relations (Requires logging in to POP)



Correspondence of content

Course Corresponds course  Description 
MAT-55216 Topics in applied mathematics, 3-5 cr MAT-55210 Topics in Applied Mathematics, 3-5 cr  
MAT-55216 Topics in applied mathematics, 3-5 cr MAT-55217 Topics in Applied Mathematics, 3-9 cr  

More precise information per implementation

Implementation Description Methods of instruction Implementation
MAT-55216 2012-01 Applied Stochastic Differential Equations (3 credits) An introduction to the theory, applications and numerical methods for SDEs. After the course the student should be able to formulate a simple SDE model for an application, analyse its properties, and solve it numerically using appropriate methods. Prerequisites knowledge: multivariate differential and integral calculus, matrix analysis, basic probability, Matlab/Octave.        

Last modified30.05.2012