Course content

The course is an introduction to chemometric methods and data analysis with emphasis on applications for chemistry, biotechnology, process chemistry, material science, and physics.
The course covers methods for the design of experiments, preprocessing and modeling of measured data to extract useful information from possibly large data sets, and use this for supporting decisions. Specifically, the following themes are covered: simple regression (the least squared method and polynomial regression), experimental design (full and fractional factorial design), preprocessing (normalization, auto-scaling, Fourier filtering, Savitsky-Golay numerical differentiation, convolution), latent variable methods (principal component analysis/singular value decomposition, principal component regression, partial least squares regression, validation of models (use of test sets, cross-validation, bootstrap and y-randomisation), cluster analysis (hierarchical cluster analysis, k-means cluster analysis and "gap" statistic), classification (random forest, CART and k-nearest neighbours), and an introduction to machine learning techniques for the themes classification, regression and cluster analysis will be given.

Learning outcome

After completing the course, the student should be able to:
(1) Set up, analyse and interpret results from an experimental design
(2) Choose what type of preprocessing methods are necessary in different situations
(3) Make use of unsupervised methods such as PCA and cluster analysis to analyse and interpret multivariate data sets
(4) Make use of regression methods such as PLS and PCR to analyse, interpret and predict multivariate data sets
(5) Make use of validation methods to check the predictive ability of various models
(6) Be able to explain the strengths and weaknesses of both supervised and unsupervised methods

Learning methods and activities

Lectures. Computer exercises and calculating exercises.

Recommended previous knowledge

Basic level in mathematics (in particular linear algebra), statistics and chemistry.

Course materials

Is given in the beginning of the course.

Credit reductions