Content, progress and pedagogy of the module

Disclaimer.
This is an English translation of the module. In case of discrepancy between the translation and the Danish version, the Danish version of the module is valid.

Learning objectives

Knowledge

• linear regression models in matrix form

• regression models including logistic regression and regularized regression (e.g., ridge and LASSO regression)

• computer-intensive methods for estimating uncertainty (e.g., the bootstrap method)

• bagging, boosting, and ensemble methods

Skills

• comparison, handling, and visualization of results and analyses from multiple models in statistical software

• identify relevant and appropriate statistical models for a given problem

Competences

• be able to reflect on and communicate about the course’s various statistical models and techniques

Type of instruction

The type of instruction is organised in accordance with the general instruction methods of the programme, cf. § 17.

Extent and expected workload

The student is expected to spend 30 hours per ECTS, which for this activity means 150 hours.

Exam

Exams

Additional information

Contact: Study Board for Computer Science via cs-sn@cs.aau.dk or 9940 8854