Modern methods of computational mathematics
Course: Applied Mathematics
Structural unit: Faculty of Computer Science and Cybernetics
            Title
        
        
            Modern methods of computational mathematics
        
    
            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
        
        
            8 Semester
        
    
            Number of ECTS credits allocated
        
        
            3
        
    
            Learning outcomes
        
        
            PLO 1. Possession of in-depth professional knowledge and practical skills to optimize the design of models of any complexity, to solve specific problems of designing intelligent information systems of different physical nature.
        
    
            Form of study
        
        
            Distance form
        
    
            Prerequisites and co-requisites
        
        
            1. Know: Mathematical analysis, functional analysis, theory of differential equations, theory of optimal control, algebra and numerical methods in the volume of the first four years of university.
2. Be able to: create programs in at least one programming language, read and analyze mathematical texts, implement mathematical algorithms. Read literature in English.
3. Have skills: working with a computer, searching for information on the Internet, using translation tools, creating presentations.
        
    
            Course content
        
        
            Developing students' understanding of methods of applied mathematics, and the ability to apply them to various problems. Within the discipline, students gain knowledge of current methods of computational mathematics, develop skills necessary for the use of such methods, train in numerical modeling of various problems.
        
    
            Recommended or required reading and other learning resources/tools
        
        
            1. Lyashko S.I., Semenov V.V., Klyushyn D.A. Special"ni pytannya optymizaciyi. Kyyiv, VPC “Kyyivs"kyj universytet”, 2015
4. Lyashko S. I. Generalized optimal control of linear systems with distributed parameters. Boston / Dordrecht / London: Kluwer Academic Publishers, 2002.  466 p.
        
    
            Planned learning activities and teaching methods
        
        
            Lectures, laboratory work, independent work.
        
    
            Assessment methods and criteria
        
        
            - semester assessment:
1. Test 1 - 25 points / 15 points
2. Test 2 - 25 points / 15 points
3. Laboratory work 1 - 25 points / 15 points
4. Laboratory work 2– 25 points / 15 points
- final grade is based on the results of work in the semester. A student receives a grade if he scored 60 or more points in the semester, successfully passing at least one module test and defending one laboratory work.
Organization of evaluation:
1. Modular test 1: up to 10 weeks of the semester.
2. Modular test 2: until the end of the semester.
2. Laboratory work 1: up to 10 weeks of the semester.
2. Laboratory work 2: until the end of the semester.
        
    
            Language of instruction
        
        
            Ukrainian
        
    Lecturers
This discipline is taught by the following teachers
                    Serhii
                    Ivanovych
                    Lyashko
                
                
                    Computational Mathematics 
Faculty of Computer Science and Cybernetics
            Faculty of Computer Science and Cybernetics
Departments
The following departments are involved in teaching the above discipline
                        Computational Mathematics
                    
                    
                        Faculty of Computer Science and Cybernetics