Content, progress and pedagogy of the module

Learning objectives

Knowledge

Students who have passed the module should be able to

• Account for general methods for multivariate data analysis (principal component analysis, multiple linear regression, principal component regression, projection on latent structures, soft independent modelling of class analogy)
• Account for methods for data preprocessing (centering, scaling, non-linear and spectroscopic preprocessing, orthogonal signal correction)
• Explain basic methods for variable selection (Selectivity ratio, VIP, interval PLS, jack-knife)
• Explain the theoretical background of these methods, their advantages and limitations as well as possible applications
• Explain how multivariate methods complement traditional statistical methods

Skills

• Explore multivariate data, find groups and trends, detect and remove outliers
• Calibrate and do proper validation of multivariate regression models, use these models for prediction
• Evaluate if data need a preprocessing and which method to apply
• Calibrate and evaluate models for data classification
• Compare different regression and classification models and find which is the best
• Use multivariate methods for analysis of real data from different applications

Type of instruction

• Lectures, classroom instruction and mini-projects

Extent and expected workload

150 hours

Exam

Exams