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.
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.

Assessment
The course is graded through

• a written exam
• two practical assignments
• homework exercises.

With grades P1 and P2 for the practical assignments, grade E for the written exam, and homework bonus HB,
the final grade F is computed as follows:

F = 0.5 * E + 0.3 * P1 + 0.2 * P2 + HB.

To pass the course, it is required that each practical assignment has grade at least 6 and the grade for the written exam is at least 5.

 - F is rounded to the nearest tenth of a point if F >= 6.0.
 - F is rounded to the nearest whole point if F < 6.0.
 - The maximum final grade is 10.

To qualify for a repair of the final result the mark needs to be at least a 4. 

1. knowledge of algorithms and data structures, at the level of the bachelor course INFODS Datastructuren.
2. successfully completed a serious programming course, such as the bachelor course INFOIMP Imperatief Programmeren
   Experience with using packages in R or Python is not sufficient.
3. knowledge of probability and statistics, at the level of INFOB3OMI Onderzoeksmethoden voor Informatica.
4. knowledge of linear algebra, such as treated in the bachelor course  INFOGR Graphics.

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