8th Summer School on Data Mining, Maastricht, The Netherlands

8-th SUMMER SCHOOL ON DATA MINING, Maastricht, The Netherlands
http://www.cs.unimaas.nl/datamining/

Summer School: Data Mining

An intensive 4-day introduction to methods and applications

Department of Knowledge Engineering, Maastricht University,
Maastricht, The Netherlands
August 30 – September 2, 2010

Introduction
Most business organizations collect terabytes of data about business
processes and resources. Usually these data provide just “facts and
figures”, not knowledge that can be used to understand and eventually
re-engineer business processes and resources. Scientific community in
academia and business have addressed this problem in the last 20 years
by developing a new applied field of study known as data mining.
In practice data mining is a process of extracting implicit,
previously unknown, and potentially useful knowledge from data. It
employs techniques from statistics, artificial intelligence, and
computer science. Data mining has been successfully applied for
acquiring new knowledge in many domains (like Business, Medicine,
Biology, Economics, Military, etc.). As a result most business
organizations need urgently data-mining specialists, and this is
the point where this course comes to help.

Course Description
The course is well balanced between theory and practice. Each lecture
is accompanied by a lab in which course participants experiment with
the techniques introduced in the lecture. The lab tool is Weka, one
of the most advanced data-mining environments. A number of real data
sets will be analysed and discussed. In the end of the course
participants develop their own ability to apply data-mining techniques
for business and research purposes.

Course Description
The course focuses on techniques with a direct practical use.
A step-by-step introduction to powerful (freeware) data-mining tools
will enable you to achieve specific skills, autonomy and hands-on
experience. A number of real data sets will be analysed and discussed.
In the end of the course you will have your own ability to apply data-
mining techniques for research purposes and business purposes.

Course Content
The course will cover the topics listed below.
– The Knowledge Discovery Process
– Data Preparation
– Basic Techniques for Data Mining:
+ Decision-Tree Induction
+ Rule Induction
+ Instance-Based Learning
+ Bayesian Learning
+ Support Vector Machines
+ Regression Techniques
+ Clustering Techniques
+ Association Rules
– Tools for Data Mining
– How to Interpret and Evaluate Data-Mining Results

Intended Audience
This course is intended for four groups of data-mining beginners:
students, scientists, engineers and experts in specific fields who need
to apply data-mining techniques to their scientific research, business
management, or other related applications.

SIKS
Participating in this course is a part of the advanced components stage
of SIKS’ educational program. SIKS has reserved a number of places for
those Ph.D-students working on the course topics.

Prerequisites
The course does not require any background in databases, statistics,
artificial intelligence, or machine learning. A general background in
science is sufficient as is a high degree of enthusiasm for new
scientific approaches.

Certificate
Upon request a certificate of full participation will be provided after
the course.

Registration
To register for the course please send an email to the registration office
specifying the following information:
– Name
– University / Organisation
– Address
– Phone
-E-Mail

Please register before August 9, 2010

Registration fees
Academic fee 600 Euros
Non-academic fee 850 Euros

Included in the price are: course material and coffee breaks. The local
cafeteria will be available for lunch (not included).

SIKS-Ph.D. students
Participating in this course is a part of the advanced components stage
of SIKS’ educational program. SIKS has reserved a number of places for
those Ph.D-students working on the course topics. SIKS-Ph.D.-students
interested in taking the course should NOT contact the local organization,
but send an e-mail to office(at)siks.nl and confirm that their supervisor
supports their participation

E-mail should be sent to: smirnov(at)maastrichtuniversity.nl

Regular mail should be sent to:

Evgueni Smirnov
Department of Knowledge Engineering
Faculty of Humanities and Sciences
Maastricht University
P.O.Box 616
6200 MD Maastricht
The Netherlands
Phone: +31 (0) 43 38 82023
Fax: +31 (0) 43 38 84897