Mathematics for biomedical data analysis
The course is meant for everyone who want to apply mathematical methods for analysis of biomedical data.
Note that this course is part of the Bioinformatics and data analysis training module. Before you can register to this course you must first apply for non-degree study right for Bioinformatics and data analysis training module!
After your application have been accepted for Bioinformatics and data analysis training module, you can register for this course by sending email to email@example.com
Mode of delivery
Course study period
This course is part of a study module:
- Bioinformatics and data analysis training module 25 ects (Academic Year 2022-2023)
Selected mathematical perspectives to introduce topical conversations and developments in the field. Algorithms: A set of simple mathematical concepts is used to classify a wide variety of computational techniques (logistic regression, principal component analysis, kernel methods, random forests, graphical models, deep learning) depending on their learning, versatility and application type, for a non-technical overview of the field. Inference: Covers the major mathematical principles underpinning data-dredging and related pitfalls, such as misinterpretation of P-values and multiple testing problems. Causality: Considers the mathematical basis for the paradigmatic shifts from traditional data analysis to causal analysis. Provoking examples are provided to 1) distinguish causal and non-causal relationships and 2) explain why causal inference is beyond the scope of the mathematical language of classical statistics.
Contents can vary.
Academic Year 2022-2023
Field of study