Data mining (2025/2026: Periode 1)

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Cursusdoel

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 digital 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.

You are allowed to retake those parts for which you did not meet the minimum required grade to pass the course, provided that the final grade registered in Osiris is 4 or 5, or "AANV."

Prerequisites
It is required that the student has:
  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.

Vakinhoudelijk

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
  • classification for social network mining
Course form
Lectures, lab sessions.

Literature
Selected book chapters, articles, and lecture notes.

Werkvormen

Hoorcollege

Werkcollege

Toetsing

Eindresultaat

Verplicht | Weging 100% | ECTS 7,5

Ingangseisen en voorkennis

Ingangseisen

Je moet een geldige toelatingsbeschikking hebben

Voorkennis

This course is aimed at students of the Computing Science (COSC) master program. It is assumed 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". (3) Knowledge of probability and statistics, at the level of "Onderzoeksmethoden voor Informatica".

Voorkennis kan worden opgedaan met

(4) Knowledge of linear algebra (such as treated in the bachelor course Graphics

Voertalen

  • Engels

Cursusmomenten

Gerelateerde studies

Tentamens

Er is geen tentamenrooster beschikbaar voor deze cursus

Verplicht materiaal

Er is geen informatie over de verplichte literatuur bekend


Aanbevolen materiaal

Er is geen informatie over de aanbevolen literatuur bekend

Coördinator

dr. A.J. Feelders A.J.Feelders@uu.nl

Docenten

dr. A.J. Feelders A.J.Feelders@uu.nl

Inschrijving

Deze cursus is open voor bijvakkers. Controleer wel of er aanvullende ingangseisen gelden.

Inschrijving
Van maandag 2 juni 2025 tot en met vrijdag 20 juni 2025

Na-inschrijving
Van maandag 18 augustus 2025 tot en met zondag 14 september 2025

Inschrijving niet geopend
Permanente link naar de cursuspagina
Laat in de Cursus-Catalogus zien