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
- Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Finalised by
- The Faculty Board of Science and Technology, 27 April 2010
- Responsible department
- Biology Education Centre
Learning outcomes
After successful completion of the course the student should be able to
- describe the theoretical basis for and being able to use some of the basic methods for exploratory multivariate data analysis: data compression and visualisation.
- describe the theoretical basis for and being able to use some of the basic methods for predictive multivariate data analyses: multidimensional regression and classification.
- interpret the results obtained using the methods referred to above.
- use experimental design methods and adjust experiments and methodologies in accordance with the resources available.
- carry out the analyses and interpretations using at leas one kind of general software environment for analysis, for example MATLAB or R.
Content
Exploratory multivariate data analysis: pre-processing, 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 (hp), Exercises (2 credits).