Recommended prerequisite for participation in the module

Content, progress and pedagogy of the module

Learning objectives

Knowledge

Students who complete the module:

• Have knowledge of statistical pattern recognition in general
• Have knowledge of methods such as neural networks, Baysian nets and rule-based systems
• Are able to describe the components of a pattern recognition system
• Are able to understand how patterns can be described from characteristic features extracted from one- or multi-dimensional data
• Have knowledge of supervised and unsupervised learning

Skills

• Are able to design and test a pattern recognition system
• Are able to apply parametric and non-parametric classification techniques to univariate and multivariate data
• Are able to analyse and describe the underlying density function of a data set
• Are able to apply methods to reduce the dimensionality of the data  
• Are able to design and test a decision support system 

Competences

• Are able to demonstrate understanding of concepts, theories and techniques in the area of statistical pattern recognition and decision support
• Are able to apply feature analysis and classification techniques to specific multi-dimensional pattern recognition problems

Type of instruction

Lectures and exercises.

Exam

Exams

Additional information

Please contact the   programme student counsellors if you consider applying to the education and you have questions.

Please consult the Moodle page for your semester and contact the semester coordinator if you have academic questions, or the study secretary if you have administrative questions.

All other enquiries may be directed to Malene Møller Knudsen, secretary of the Study Board.