Prerequisite/Recommended prerequisite for
participation in the module
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|