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** 12th MAASTRICHT SCHOOL ON DATA SCIENCE, **
** Department of Data Science and Knowledge Engineering **
** Maastricht University, **
** Maastricht, The Netherlands **
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** June 26 - June 28, 2019 **
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** https://project.dke.maastrichtuniversity.nl/datamining/ **
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** Apologies if you receive multiple copies of this announcement **
** Please forward to anyone who might be interested **
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School on Data Science
An intensive 3-day introduction to methods and applications of data science
Department of Data Science and Knowledge Engineering, Maastricht University,
Maastricht, The Netherlands
June 26 - June 28, 2019
Introduction
With the promises of Data Science, modern organisations collect terabytes of
data related to their function, market share, business activities, and daily
operation. Usually the data collected provides only a low level view of the
"facts and figures", and Data Science can help to translate this data into
knowledge that can be used to understand and eventually improve the overall
performance of the organisations. Data Science employes techniques from
statistics, machine learning, artificial intelligence, and computer science.
It has been successfully applied in many domains including Business, Medicine,
Biology, Economics, Military, etc. As a result, most organisations are
starting to recognise their need for data scientists specialists, and this is
where this school can be of help.
Description
Our school on data science balances both theory and practice. We think it is
important to not only learn to use the available techniques, but to also apply
them correctly and when appropriate. Each lecture is accompanied by a lab
in which participants experiment with the techniques introduced in the lecture.
A number of real data sets will be analysed and discussed. Throughout the
duration of the school participants develop their own abilities to safely and
reliably apply data-science techniques for business and research purposes.
Content
The school will cover the topics listed below.
- Knowledge Discovery Process
+ Data Collection and Preparation
+ Feature Selection: Filters, Wrappers, Embedded Methods
- Machine Learning Techniques:
+ Regression: Linear Regression, Ridge Regression, LASSO,
Nearest Neighbor Regression, Regression Trees,
Model Trees
+ Classification: Logistic Regression, Support Vector Machines,
Decision Trees, Decision Rules, Nearest Neighbor
Classification
+ Clustering: k-means, hierarchical clustering, DBSCAN, and
validation
+ Association rules: Apriori, frequent item mining, rule generation
+ Deep Learning: Classical Feedforward Neural Nets, Convolutional
Neural Networks, Regularization, Recurrent Neural
Networks, and GANs
- Model Validation: Hold-out Validation, Cross Validation, ROC Analysis etc.
Intended Audience:
This school is intended for four groups of data-science beginners: students,
scientists, engineers, and experts in specific fields who need to apply data-
science techniques to their scientific research, business management, or other
related applications.
Prerequisites:
No special knowledge in statistics, artificial intelligence, or machine learning
is required. An affinity with science is sufficient as is a high degree of enthusiasm
for new scientific approaches.
Registration:
To register for the school please send an email to:
[email protected]
[email protected]
(Please send the registration to both e-mails)
In the e-mail please specify: your name, university/organisation, address, phone, e-mail
Registration Deadline: June 24, 2019
Registration fees:
Academic fee 840 Euros
Non-academic fee 1680 Euros
(to be paid after the School)
Contact:
Evgueni Smirnov and Kurt Driessens
Department of Data Science and 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-mails: [email protected]
[email protected]
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