Main field(s) of study and in-depth level:
Computer Science G1F,
Explanation of codes
The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:
G1N: has only upper-secondary level entry requirements
G1F: has less than 60 credits in first-cycle course/s as entry requirements
G1E: contains specially designed degree project for Higher Education Diploma
G2F: has at least 60 credits in first-cycle course/s as entry requirements
G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
GXX: in-depth level of the course cannot be classified.
A1N: has only first-cycle course/s as entry requirements
A1F: has second-cycle course/s as entry requirements
A1E: contains degree project for Master of Arts/Master of Science (60 credits)
A2E: contains degree project for Master of Arts/Master of Science (120 credits)
AXX: in-depth level of the course cannot be classified.
Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
The Faculty Board of Science and Technology
describe the fundamental concepts discretisation, accuracy and order of accuracy, efficiency, stability, discretisation errors (truncation error), ansatz, adaptivity;
generally explain the idea behind the algorithms that are presented in the course;
describe the fundamental difference between stochastic and deterministic algorithms;
analyse the order of accuracy and stability properties for basic numerical methods and understand how such an analysis is employed;
evaluate methods with respect to accuracy, stability properties and efficiency;
based on such evaluation, discuss the suitability of methods given different different applications;
given a mathematical model, solve problems in science and engineering by structuring the problem, choose appropriate numerical method and generate solution using software and by writing programming code;
present, explain, summarise, evaluate and discuss solution methods and results in a short report.
Continued programming in MATLAB, with specialisation in visualisation. Continued problem solving methodology. Data analysis: least squares problems with solution based on the normal equations and QR decomposition. Interpolation with an emphasis on piecewise interpolation (including cubic spines). Solution to ordinary differential equations (initial-value problem). Adaptivity. Stability. Explicit and implicit methods and in connection with this solution to non-linear equation systems. Monte Carlometoder and methods based on random number.
Lectures, problem classes, laboratory work, compulsory assignments.
Written examination at the end of the course and compulsory assignments.