9600-0006/02 – Complex Systems Modelling (MSS)
Gurantor department | IT4Innovations | Credits | 10 |
Subject guarantor | prof. Ing. Ivo Vondrák, CSc. | Subject version guarantor | prof. Ing. Ivo Vondrák, CSc. |
Study level | postgraduate | Requirement | Choice-compulsory |
Year | | Semester | winter + summer |
| | Study language | English |
Year of introduction | 2015/2016 | Year of cancellation | |
Intended for the faculties | USP, FEI | Intended for study types | Doctoral |
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:
Recommended literature:
Way of continuous check of knowledge in the course of semester
E-learning
Other requirements
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
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
Předmět neobsahuje žádné hodnocení.