Content, progress and pedagogy of the module

Learning objectives

Knowledge

Students who complete the module must 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, nonlinear and spectroscopic reprocessing, 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 identify the best
• use multivariate methods for analysis of real data from different applications

Type of instruction

• Lectures
• Workshops
• Exercises
• Mini-projects

Extent and expected workload

150 hours

Exam

Exams