[edm-announce] Fwd: Data Mining School in Maastricht, The Netherlands
- From: "Ryan S.J.d. Baker" <rsbaker@xxxxxxx>
- To: edm-announce@xxxxxxxxxxxxx
- Date: Fri, 8 Jul 2011 23:15:46 -0400
---------- Forwarded message ----------
From: Smirnov E (DKE) <smirnov@xxxxxxxxxxxxxxxxxxxxxxx>
Date: 8 July 2011 09:23
Subject: Data Mining School in Maastricht, The Netherlands
Dear Dr. Colleagues,
Please find attached the CFP of the 9-th Data Mining School in
Maastricht, The Netherlands, Aug 29 - Sep 1. We would like to ask you
to make the CFP available on www.educationaldatamining.org.
Thank you in advance
Best regards
Evgueni Smirnov
*********************************************************************
** **
** 9-th SUMMER SCHOOL ON DATA MINING, Maastricht, The Netherlands **
** http://www.unimaas.nl/datamining/ **
** **
** Apologies if you receive multiple copies of this announcement **
** Please forward to anyone who might be interested **
** **
*********************************************************************
Summer School: Data Mining
An intensive 4-day introduction to methods and applications
Department of Knowledge Engineering, Maastricht University,
Maastricht, The Netherlands
August 29 - September 1, 2011
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 school comes to help.
Description
The school curricullum is well balanced between theory and practice.
Each lecture is accompanied by a lab in which 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 school
participants develop their own ability to apply data-mining techniques
for business and research purposes.
Tools
The school 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 school you will have your own ability to apply data-
mining techniques for research purposes and business purposes.
Content
The school 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 school 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.
Prerequisites
The school 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 school.
Registration
To register for the school please send an email to:
smirnov@xxxxxxxxxxxxxxxxxxxxxxx
In the e-mail please specify:
- Name
- University / Organisation
- Address
- Phone
-E-Mail
Registration Deadline: August 22, 2011
Registration fees
Academic fee 600 Euros
Non-academic fee 850 Euros
Included in the price are: school material and coffee breaks. The local
cafeteria will be available for lunch (not included).
Registartion e-mail: smirnov@xxxxxxxxxxxxxxxxxxxxxxx
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
E-mail: smirnov@xxxxxxxxxxxxxxxxxxxxxxx
*********************************************************************
** **
** 9-th SUMMER SCHOOL ON DATA MINING, Maastricht, The Netherlands **
** http://www.unimaas.nl/datamining/ **
** **
** Apologies if you receive multiple copies of this announcement **
** Please forward to anyone who might be interested **
** **
*********************************************************************
Summer School: Data Mining
An intensive 4-day introduction to methods and applications
Department of Knowledge Engineering, Maastricht University,
Maastricht, The Netherlands
August 29 - September 1, 2011
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 school comes to help.
Description
The school curricullum is well balanced between theory and practice.
Each lecture is accompanied by a lab in which 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 school
participants develop their own ability to apply data-mining techniques
for business and research purposes.
Tools
The school 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 school you will have your own ability to apply data-
mining techniques for research purposes and business purposes.
Content
The school 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 school 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.
Prerequisites
The school 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 school.
Registration
To register for the school please send an email to:
smirnov@xxxxxxxxxxxxxxxxxxxxxxx
In the e-mail please specify:
- Name
- University / Organisation
- Address
- Phone
-E-Mail
Registration Deadline: August 22, 2011
Registration fees
Academic fee 600 Euros
Non-academic fee 850 Euros
Included in the price are: school material and coffee breaks. The local
cafeteria will be available for lunch (not included).
Registartion e-mail: smirnov@xxxxxxxxxxxxxxxxxxxxxxx
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
E-mail: smirnov@xxxxxxxxxxxxxxxxxxxxxxx
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