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Course module: INFOMDM
INFOMDM
Data mining
Course infoSchedule
Course codeINFOMDM
ECTS Credits7.5
Category / LevelM (M (Master))
Course typeCourse
Language of instructionEnglish
Offered byFaculty of Science; Graduate School of Natural Sciences; Graduate School of Natural Sciences;
Contact persondr. A.J. Feelders
Telephone+31 30 2533176
E-mailA.J.Feelders@uu.nl
Lecturers
Course contact
dr. A.J. Feelders
Other courses by this lecturer
Lecturer
dr. A.J. Feelders
Other courses by this lecturer
Teaching period
1-GS  (05/09/2022 to 11/11/2022)
Teaching period in which the course begins
1-GS
Time slotD: D (WED-afternoon, Friday)
Study mode
Full-time
Enrolment periodfrom 30/05/2022 up to and including 24/06/2022
Course application processOsiris Student
Enrolling through OSIRISYes
Enrolment open to students taking subsidiary coursesYes
Pre-enrolmentNo
Post-registrationYes
Post-registration openfrom 22/08/2022 up to and including 19/09/2022
Waiting listYes
Course placement processadministratie onderwijsinstituut
Course goals
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):
(1) Classification Tree Algorithms, Bagging and Random Forests
(2) Graphical Models (including Bayesian Networks)
(3) Frequent Pattern Mining
(4) Text Mining
(5) 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 two practical assignments.
Content

This course is aimed at students of the Computing Science (COSC) master program.

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
Course form
Lectures and Computer Lab.

Literature
Selected book chapters, articles, and lecture notes.
Competencies
-
Entry requirements
You must meet the following requirements
  • Assigned study entrance permit for the master
Prerequisite knowledge
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".
Prerequisite knowledge can be obtained through
4) Knowledge of linear algebra (such as treated in the bachelor course Graphics
Required materials
-
Instructional formats
Lecture

Seminar

Tests
Final result
Test weight100
Minimum grade-

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Kies de Nederlandse taal