Entry requirements

60 credits including a programming course in Python (eg Computer programming I) and Algebra and Geometry/Linear Algebra I. Participation in one of the courses Introduction to Scientific Computing and Scientific Computing I. Participation in Probability and Statistics. Participation in Linear Algebra II/Linear Algebra for Data Analysis or the course can be taken in parallel.

Learning outcomes

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

• account for and perform tasks that require knowledge of the key concepts included in the course;
• describe, use and implement the algorithms included in the course;
• analyze the computational and memory complexity of different algorithms;
• solve technical and scientific problems given the mathematical model, by structuring the problem, choosing the appropriate numerical method, and generating a solution using software and your own code;
• present, explain, summarize, evaluate and discuss solution methods and results in a small report.

Content

Stochastic models, Monte-Carlo methods, Inverse Transform Sampling (ITS), stochastic simulation, Gillespies algorithm. Least square approximation with application to linear systems and regression models. QR factorization and orhogonalizations. Methods for eigenvalues and eigenvectors (power method and QR method). Singular value decomposition (SVD) and applications. Important key concepts included in the course include stochastic/deterministic model and method, matrix factorization, singular values and singular vectors.

Instruction

Lectures, laboratory exercises, problem solving sessions.

Assessment

Written exam (3 hp). Problem solving. Assignments with a written report (2 hp).

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.