Entry requirements

Alt. 1) 120 credits in the engineering programme in molecular biotechnology including Multivariate data analysis and experimental design. Alt. 2) 120 credits. Bioinformatics - starting course (can be read in parallel).

Learning outcomes

After passing the course the student should be able to

• account for and apply classical statistical inference based on Bayesian and frequentist methods, traditional computer-based methods, as well as computer-intensive methods, for analysis one variable alone and several variables at the same time
• choose and apply the appropriate among above-mentioned methods and technologies for statistical inference for a given set of biological and biomedical molecular data and their associated biomedical questions.

Content

Classical inference: Frequentist and Bayesian inference, maximum likelihood estimation. Traditional computer-based methods: Empirical Bayes, ridge regression, generalized linear models, regression trees, survival analysis and the EM-algorithm. Computer-intensive methods as resampling, resampling based confidence intervals, cross validation, large-scale hypothesis testing, sparse regression models, random forests, and boosting. Bioinformatic application examples.

Instruction

Lectures, calculation exercises and computer exercises.

Assessment

Written examination (3 credits), computer exercises (2 credits).