School description
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 keeps a
balance 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.
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.
SIKS
Participating in this school 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 topics
related to the school.
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
.
