Entry requirements
Human Centered Multimedia (MSc Information Studies)
Direct admission
You are directly admitted for the HCM track programme, if you have an average final grade of 7 or higher and a Dutch academic Bachelor's degree in Information Studies (Informatiekunde).
Relevant Bachelor's degrees
You will qualify for the Human Centered Multimedia track of the Master Programme Information Studies if you have a relevant bachelor's degree and an average final grade of 7 or higher*.
Typical examples of bachelor programmes that may provide access to our master programme are:
- Information Studies
- Lifestyle Informatics
- Communication Sciences
- Media Studies
- Media, Information and Communication
Bachelors graduates from Universities of Applied Sciences (HBOs) will have to follow a pre-master course in Academic Skills before starting the master programme.
Students that have a Bachelor's degree in a related field, may qualify after following a pre-master programme.
If you have a Bachelor's degree in a different field than mentioned above, you may also be eligible for this programme, due to e.g. work experience. Please contact the HCM Programme Manager if you have any questions concerning your eligibility.
*Dutch grading system; equivalent of 3.0 in US system, 2:1 in UK system, C in ECTS system.
Pre-master
Examples of related bachelor programmes and corresponding pre-master courses are:
This table is intended to give an indication. Students will be admitted on individual basis, depending on their study programme and their results and motivation.
Please check the section 'Pre-Master's programme' for details.
Required knowledge and skills
Students that enter our master programme need to posses the knowledge and skills on the topics that are listed below. Students that can prove to have acquired knowledge and skills during their studies may ask for exemption of the pre-master modules.
Data Mining
Basic understanding of database theory, theory about supervised and
unsupervised learning and
probabilities and entropy.
- Calculate the entropy of a probability distribution
- Bayes’ rule.
- Basic probability axioms (Non-negativity, additivity and normalization)
Linear regression
- Calculate the sum squared errors measure
- Calculate the sum absolute errors measure
- Compare data points on their contribution to the error measure.
Decision trees
- Evaluation (cross-validation, classifier evaluation, training, test data sets)
- Overfitting (definition, Occam's Razor, pruning, reduced-error pruning)
- Extensions (decision trees with continuous-valued attributes, deal with missing discrete or continuous values, regression trees to predict numerical classes)
Neural Networks
- Perceptron and perceptron learning
- Learning rate
- Gradient descent algorithm and stochastic gradient descent algorithm
k-Nearest Neighbors
- The k-NN algorithm
- Distance-weighted voting, majority
- k-NN versus prototype classification
- Ties
Knowledge Web
Communication theory (data, information, knowledge), Epistemology (Belief, truth, justification, internalism, externalism), Acquiring knowledge ( a priori, a posteriori, empiricism, rationalism, constructivism) & RDF
- Resources and data types
- Property, statement, graph
- RDF syntaxes (Turtle, RDF, RDFa)
RDFS
- Classes and properties
- Class hierarchies and inheritance
- Constraints
Querying
- SPARQL infrastructure
- Basic query, matching , filter
- Result sets
Research methods and academic skills
- Statistics
- Basic research methods
- Academic writing