Recommended prerequisite for participation in
The course builds on knowledge obtained during the bachelor courses
in “Linear Algebra” and “Introduction to Probability and Applied
Content, progress and pedagogy of the
- Must have knowledge of data modelling in form of preparing
data, modelling data, and evaluating and disseminating the
- Must have knowledge about key machine learning concepts such as
feature extraction, cross-validation, generalization and
over-fitting, prediction and curse of dimensionality.
- Must have knowledge about different machine learning
principles, algorithms, techniques and be able to define and
describe fundamental problems and consequences within machine
- Must have knowledge about basic recommender system principles,
techniques, algorithms and be able to define and describe
fundamental problems and consequences within these.
- Must be able to discuss how the data modelling methods work and
describe their assumptions and limitations.
- Must be able to map practical problems to standard data models
such as regression, classification, density estimation, clustering
and association mining.
- Must be able to select and apply a range of different machine
learning algorithms and techniques on specific problems.
- Must be able to select and apply the basic recommender system
algorithms and techniques on specific problems.
- Must have the competency to solve machine learning related
problems in a practical context.
- Must have the competency to apply machine learning algorithms
and analyse the results
Type of instruction
Types of instruction are listed at the start of
§17; Structure and contents of the programme.
|Name of exam||Machine Learning|
|Type of exam|
Written or oral exam
|Assessment||7-point grading scale|
|Type of grading||Internal examination|
|Criteria of assessment||The criteria of assessment are stated in the Examination
Policies and Procedures|