Multivariate Data Analysis and Experimental Design
5 credits
Syllabus, Bachelor's level, 1MB344
A revised version of the syllabus is available.
- Code
- 1MB344
- Education cycle
- First cycle
- Main field(s) of study and in-depth level
- Computer Science G2F, Technology G2F
- Grading system
- Pass with distinction (5), Pass with credit (4), Pass (3), Fail (U)
- Finalised by
- The Faculty Board of Science and Technology, 28 April 2011
- Responsible department
- Biology Education Centre
Learning outcomes
After successful completion of the course the student should be able to
- use and theoretically describe some of the basic methods for exploratory multivariate data analysis: Examples: 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: Examples: Conventional least square adjustments for linear models and design of linear discriminants for classification.
- use basic experimental design methods. Examples: 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
Theory (3 credits), Exercises (2 credits).