Data Mining Techniques


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

Teaching Methods

Lectures (h) and compulsory practical work (pra). Lectures are planned
to be interactive: there will be small questions, etc.

Method of Assessment

Practical assignments (i.e. there is no exam). There will be two
assignments done in groups of three. There is a possibility to get a
grade without doing these assignments: to do a real research project
instead (which will most likely to involve more work, but it can also be
more rewarding). For the regular assignments the first assignment counts
for 40% and the second for 60%. The grade of both assignments needs to
be sufficient to pass the course.


Ian H. Witten, Eibe Frank, Mark A. Hall, Data Mining: Practical Machine
Learning Tools and Techniques (Third Edition). Morgan Kaufmann, January
2011 ISBN 978-0-12-374856-0. Also the second and fourth edition can be

Target Audience

mBA, mCS, mAI, mBio

Recommended background knowledge

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

General Information

Course Code X_400108
Credits 6 EC
Period P5
Course Level 500
Language of Tuition English
Faculty Faculty of Science
Course Coordinator dr. M. Hoogendoorn
Examiner dr. M. Hoogendoorn
Teaching Staff dr. M. Hoogendoorn

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Lecture, Computer lab
Target audiences

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