9600-0006/02 – Complex Systems Modelling (MSS)

Gurantor departmentIT4InnovationsCredits10
Subject guarantorprof. Ing. Ivo Vondrák, CSc.Subject version guarantorprof. Ing. Ivo Vondrák, CSc.
Study levelpostgraduateRequirementChoice-compulsory
YearSemesterwinter + summer
Study languageEnglish
Year of introduction2015/2016Year of cancellation
Intended for the facultiesUSPIntended for study typesDoctoral
Instruction secured by
LoginNameTuitorTeacher giving lectures
VON05 prof. Ing. Ivo Vondrák, CSc.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 2+0
Combined Examination 10+0

Subject aims expressed by acquired skills and competences

The aim of the subject is to introduce PhD students to the field of modelling, simulation, and analysis of complex systems using currently available methods and technologies under development.

Teaching methods

Lectures
Individual consultations

Summary

The course consists of discussing algorithms in the field of modelling, complex systems simulations and resulting experimental big data sets analysis. Further, methods for system modelling will also be discussed, and prime categories of tasks in the area of their continuous, discrete, and combined simulations will be defined. Then, students will be introduced to languages based on semiformal (UML) or formal approaches (Petri net, Pi-calculus). Planning and subsequent realization of simulation experiments lead to big data sets, which then need to be analysed using methods based on neuron meshes, nearest neighbour method in highly dimensional data, flow data processing, identification of association rules, clustering, algorithms of analysis and big graphs structure detection, techniques for obtaining important characteristics from big data sets using reduction of dimension and algorithms of machine learning such as perceptron meshes and SVM (Support Vector Machines). Within the course, an emphasis will be put on applying methods optimized for HPC servers and methods which are currently being developed for accelerators.

Compulsory literature:

• Kreuzer, W., System simulation, programming styles and languages, Addison Wesley, 1986 • Jure Leskovec, Anand Rajaraman, Jeff Ullman: Mining of Massive Datasets, Cambridge University Press, 2014, ISBN 978-1107077232

Recommended literature:

• Wil van der Aalst, Kees van Hee: Worklflow Management, Models, Methods, and Systems. MIT Press, 2002 • Guojun Gan, Chaoqun Ma, Jianhong Wu: Data Clustering: Theory, Algorithms, and Applications, SIAM, Society for Industrial and Applied Mathematics, 2007, ISBN 978-0898716238

Way of continuous check of knowledge in the course of semester

E-learning

Další požadavky na studenta

No other requirements.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

The course consists of discussing algorithms in the field of modelling, complex systems simulations and resulting experimental big data sets analysis. Further, methods for system modelling will also be discussed, and prime categories of tasks in the area of their continuous, discrete, and combined simulations will be defined. Then, students will be introduced to languages based on semiformal (UML) or formal approaches (Petri net, Pi-calculus). Planning and subsequent realization of simulation experiments lead to big data sets, which then need to be analysed using methods based on neuron meshes, nearest neighbour method in highly dimensional data, flow data processing, identification of association rules, clustering, algorithms of analysis and big graphs structure detection, techniques for obtaining important characteristics from big data sets using reduction of dimension and algorithms of machine learning such as perceptron meshes and SVM (Support Vector Machines). Within the course, an emphasis will be put on applying methods optimized for HPC servers and methods which are currently being developed for accelerators.

Conditions for subject completion

Full-time form (validity from: 2015/2016 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Examination Examination  
Mandatory attendence parzicipation:

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.FormStudy language Tut. centreYearWSType of duty
2019/2020 (P2658) Computational Sciences (2612V078) Computational Sciences P English Ostrava Choice-compulsory study plan
2019/2020 (P2658) Computational Sciences (2612V078) Computational Sciences K English Ostrava Choice-compulsory study plan
2018/2019 (P2658) Computational Sciences (2612V078) Computational Sciences P English Ostrava Choice-compulsory study plan
2018/2019 (P2658) Computational Sciences (2612V078) Computational Sciences K English Ostrava Choice-compulsory study plan
2017/2018 (P2658) Computational Sciences (2612V078) Computational Sciences P English Ostrava Choice-compulsory study plan
2017/2018 (P2658) Computational Sciences (2612V078) Computational Sciences K English Ostrava Choice-compulsory study plan
2016/2017 (P2658) Computational Sciences (2612V078) Computational Sciences P English Ostrava Choice-compulsory study plan
2016/2017 (P2658) Computational Sciences (2612V078) Computational Sciences K English Ostrava Choice-compulsory study plan
2015/2016 (P2658) Computational Sciences (2612V078) Computational Sciences P English Ostrava Choice-compulsory study plan
2015/2016 (P2658) Computational Sciences (2612V078) Computational Sciences K English Ostrava Choice-compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner