Entry requirements

60 credits within the Master's Programme in Molecular Biotechnology Engineering including Scientific Computing I, Probability and Statistics, and Linear Algebra II. Participation in Programming Techniques II.

Learning outcomes

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

• use and theoretically describe some of the basic methods for exploratory multivariate data analysis; data compression and visualisation using principal-component analysis and cluster analysis .
• use and theoretically describe some of the basic methods for predictive multivariate data analysis; conventional least square adjustments for linear models and design of linear discriminants for classification.
• use basic experimental design methods; factorial design and respons surface designe in two levels.
• carry out and implement the analyses and interpretations using at least one general software environment for the analyses, for example MATLAB or R.

Content

Exploratory multivariate data analysis: principal component analysis, clustering etc. Predictive multivariate data analyses: model based and model-free linear/nonlinear classification and regression, model selection and performance estimation. Applications.

Instruction

Lectures, seminars, exercises based on manual as well as computer calculations.

Assessment

Computer exercises, assignments and written exam.

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.