Data Mining Techniques

  • Course code: X_400108
  • Period: Period 5
  • Credits: 6.0
  • Language of tuition: English
  • Faculty: Faculteit der Exacte Wetenschappen
  • Coordinator: dr. M. Hoogendoorn
  • Teaching staff: dr. M. Hoogendoorn
  • Teaching method(s): Lecture
  • Level: 500

Course objective

The aim of the course is that students acquire data mining knowledge
and skills that they can apply in a business environment.
How the aims are to be achieved: Students will acquire knowledge and
skills mainly through the following: an overview of the most common
data mining algorithms and techniques (in lectures), a survey of
typical and interesting data mining applications, and practical
assignments to gain "hands on" experience. The application of skills in
a business environment will be simulated through various assignments of
the course.

Course content

The course will provide a survey of basic data mining techniques and
their applications for solving real life problems. After a general
introduction to Data Mining we will discuss some "classical" algorithms
like Naive Bayes, Decision Trees, Association Rules, etc., and some
recently discovered methods such as boosting, Support Vector Machines,
and co-learning. A number of successful applications of data mining
will also be discussed: marketing, fraud detection, text and Web
mining, possibly bioinformatics. In addition to lectures, there will be
an extensive practical part, where students will experiment with
various data mining algorithms and data sets. The grade for the course
will be based on these practical assignments (i.e., there will be no
final examination).

Form of tuition

Lectures and compulsory practical work. Lectures are planned to be
interactive: there will be small questions, one-minute discussions, etc.

Type of assessment

Practical assignments (i.e. there is no exam). There will be three
assignments, some (parts) of these will be done individually, some in
groups of two. There is a possibility to get a grade without doing
these assignments: one (!) group can be selected (based on interviews
conducted by the lecturer) to do a real research project instead (which
- be warned - will most likely to involve more work, but it can also be
more rewarding).

Course reading

Ian H. Witten, Eibe Frank, Mark A. Hall, Data Mining: Practical Machine
Learning Tools and Techniques (Third Edition). Morgan Kaufmann, January
ISBN 978-0-12-374856-0

Entry requirements

Kansrekening en Statistiek of Algemene Statistiek (knowledge of
statistics and probabilities) or equivalent. Recommended: Machine

Target group

mBA, mCS, mAI, mBio

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