Modern methods of computer modeling
Course: Applied Mathematics
Structural unit: Faculty of Computer Science and Cybernetics
            Title
        
        
            Modern methods of computer modeling
        
    
            Code
        
        
            Module type 
        
        
            Вибіркова дисципліна для ОП
        
    
            Educational cycle
        
        
            First
        
    
            Year of study when the component is delivered
        
        
            2021/2022
        
    
            Semester/trimester when the component is delivered
        
        
            6 Semester
        
    
            Number of ECTS credits allocated
        
        
            2
        
    
            Learning outcomes
        
        
            PLO 9 Combine methods of mathematical computer modeling with informal expert analysis procedures to find optimal solutions. Modern methods of computer modeling.
PLO 10. Build algorithms that are efficient in terms of calculation accuracy, stability, speed and cost of system resources for numerical study of mathematical models and solving practical problems.
PLO 14. Use specialized software products and software systems of computer mathematics in practical work.
        
    
            Form of study
        
        
            Distance form
        
    
            Prerequisites and co-requisites
        
        
            Know: basic concepts and concepts of programming, algebra, mathematical analysis, discrete mathematics, theory of algorithms at the basic level (the volume of the second year of university), the essence of the concept of algorithm.
Be able to: create programs in any language, read and analyze mathematical texts, implement simple algorithms.
Have basic skills: working with a computer, searching for information on the Internet, using translation systems.it. 
        
    
            Course content
        
        
            Module 1. Problems of cluster analysis, study of basic approaches to building cluster analysis algorithms. Lec. -12 g. Ind.work -14 g.
General characteristics of clustering methods Basic approaches to algorithm development.
K-means method, its field of application.
Software implementation of the K-means method and its test.
Fuzzy K-means method, its advantages over the K-means method. Software implementation.
Other clustering methods such as Db Scan and grid type methods.
Module 2 Natural algorithms of evolutionary type of search of extremum of functions.
Lectures -16 g. Independent work -20.
General characteristics of nature-like algorithms of evolutionary type.
Study of the particle swarm method for finding the extremum of a function.
Testing algorithms on functions of several variables.
Study of the evolutionary algorithm of the genetic type.
Software implementation of genetic algorithm.
Testing of genetic algorithm on Rosenbock, Rastrigin, Eckley functions.
        
    
            Recommended or required reading and other learning resources/tools
        
        
            Planned learning activities and teaching methods
        
        
            Lectures, independent work, elaboration of recommended literature, implementation of projects.
        
    
            Assessment methods and criteria
        
        
            Intermediate assessment:
The maximal number of available points is 60.
1. Individual project 1 РН1.1, РН1.2, РН1.3, РН 2.1, РН2.2, РН2.3 - 20 points / 12 points
2. Individual project 2 РН1.1, РН1.2, РН1.3, РН 2.1, РН2.2, РН2.3 - 20 points / 12 points
3. Individual project 3 РН1.1, РН1.2, РН1.3, РН 2.1, РН2.2, РН2.3 - 20 points / 12 points
Final assessment (in the form of exam): 
The maximal number of available points is 40.
The results of study to be assessed are RN 1.1, RN 1.2, RN 1.3, RN 2.1, RN 2.2 and RN 2.3.
- form and types of tasks: written with the defense of the answer, two theoretical questions of 10 points and two practical tasks (10 points).
        
    
            Language of instruction
        
        
            Ukrainian
        
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