After this course the student knows how several well-known data mining algorithms work, how and when they can be applied,
and how the resulting models and patterns should be interpreted.

Topics covered include (content can vary somewhat from year to year):

• Classification Tree Algorithms, Bagging and Random Forests
• Graphical Models (including Bayesian Networks)
• Frequent Pattern Mining
• Text Mining
• Social Network Mining

Furthermore, the student understands general problems of data-analysis, such as overfitting, the curse of dimensionality, and model selection.
Finally, the student gains practical experience with the programming and application of data mining algorithms through practical assignments.

Grading

Written exam and practical assignments.

This course is aimed at students of the Computing Science (COSC) master program. It is required that the student has:

1. Knowledge of algorithms and data structures, at the level of the bachelor course "Datastructuren".
2. Successfully completed a serious programming course, such as the bachelor course "Imperatief Programmeren".
   Experience with using packages in R or Python is not sufficient.
3. Knowledge of probability and statistics, at the level of "Onderzoeksmethoden voor Informatica".
4. Knowledge of linear algebra (such as treated in the bachelor course "Graphics").