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Jožef Stefan
International
Postgraduate School

Jamova 39
SI-1000 Ljubljana
Slovenia

Phone: +386 1 477 31 00
Fax: +386 1 477 31 10
Email: info@mps.si

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Course Description

Computational Scientific Discovery from Structured, Spatial and Temporal Data

Program

Information and Communication Technologies, third-level study programme

Lecturers:

prof. dr. Sašo Džeroski

Goals:

The goal of the course is to familiarize the student with the fields of computational scientific discovery and mining complex data, including structured, spatial and temporal data.
The competencies of the students completing this course successfully would include understanding of basic concepts from both areas, familiarity with state-of-the art methods, and knowledge of examples applications from two major scientific fields (environmental and life sciences).

Content:

Scientific Method:
scientific knowledge structures, scientific
activities/processes

Computational Scientific Discovery:
introduction, history of development of the area, basic methods, e.g., equation discovery, discovering networks, discovering pathways, inductive process modelling

Mining Scientific Data:
specific requirements
for mining scientific data vs. data mining in business, finance, retail; inductive databases

Mining Structured, Spatial and Temporal Data:
methods for structured output prediction and mining spatial/temporal data

Applications in Environmental Sciences:
habitat modeling, modeling population
dynamics

Applications in Life Sciences:
applications in bioinformatics, biomedicine
and systems biology, e.g., predicting gene
function, discovering metabolic and
regulation pathways

Course literature:

Selected chapters from the following books:

• S. Džeroski, and L. Todorovski, Eds. Computational Discovery of Scientific Knowledge. Springer, 2007. ISBN 978-3-540-73919-7
• T. Hey, S. Tansley, and K. Tolle, Eds. The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research, 2009. ISBN 978-0-982-54420-4
• M. Gaber, Ed. Scientific Data Mining and Knowledge Discovery: Principles and Foundations. Springer, 2010. ISBN 978-3-642-02787-1
• S. Džeroski, B. Goethals, and P. Panov, Eds. Inductive Databases and Constraint-Based Data Mining. Springer, 2010. ISBN 978-1-4419-7737-3

Significant publications and references:

• E. Ikonomovska, J. Gama, and S. Džeroski. Online tree-based ensembles and option trees for regression on evolving data streams. Neurocomputing 150, 458-470, 2015.
• P. Panov, L. Soldatova, and S. Džeroski. Ontology of core data mining entities. Data Mining and Knowledge Discovery 28 (5-6), 1222-1265, 2014.
• D. Kocev, C. Vens, J. Struyf, and S. Džeroski. Tree ensembles for predicting structured outputs. Pattern Recognition 46 (3), 817-833, 2013.
• D. Čerepnalkoski, K. Taškova, L. Todorovski, N. Atanasova, and S. Džeroski. The influence of parameter fitting methods on model structure selection in automated modeling of aquatic ecosystems. Ecological Modelling 245, 136-165, 2012.
• G. Madjarov, D. Kocev, D. Gjorgjevikj, and S. Džeroski. An extensive experimental comparison of methods for multi-label learning. Pattern Recognition 45 (9), 3084-3104, 2012.

Examination:

Seminar work (50%)
Oral defense of seminar work (50%)

Students obligations:

Seminar work
Oral defense of seminar work

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