Content, progress and pedagogy of the module

Learning objectives

Knowledge

• dynamical linear models, including the Kalman filter 
• population methods, specifically evolutionary computing and genetic algorithms
• meta analysis 
• robust statistical methods including non-parametric models
• factor analysis 
• graphical models, including hierarchical models

Skills

• can apply the relevant methodologies to one or more datasets by using appropriate software implementations, and interpret the output and modify the model parameters accordingly
• are able to state the underlying assumptions and argue about limitations and extendibility of the methodology in one or more specific settings
• can assess goodness-of-fit for the models where appropriate

Competences

• can acquire supplementary knowledge about the relevant methodologies 
• can combine appropriate topics from the course to analyse a specific dataset.
• can in writing describe the methodologies, results and outcome from an analysis of a specific dataset

Type of instruction

Lectures with exercises.

Extent and expected workload

This is a 5 ECTS course module and the work load is expected to be 150 hours for the student.
 

Exam

Exams