Information processing technologies
Course: Applied mathematics
Structural unit: Faculty of Computer Science and Cybernetics
            Title
        
        
            Information processing technologies
        
    
            Code
        
        
            ДВС.2.02
        
    
            Module type 
        
        
            Вибіркова дисципліна для ОП
        
    
            Educational cycle
        
        
            Second
        
    
            Year of study when the component is delivered
        
        
            2021/2022
        
    
            Semester/trimester when the component is delivered
        
        
            3 Semester
        
    
            Number of ECTS credits allocated
        
        
            8
        
    
            Learning outcomes
        
        
            PLO12.2. Have knowledge of the mathematical modeling and optimal management fundamentals, to the extent necessary for the development of applied disciplines and use the relevant knowledge in the chosen profession. PLO14.2. Be able to apply professional knowledge, skills and abilities in the field of applied mathematics and computer science for research of real processes of different nature.
        
    
            Form of study
        
        
            Full-time form
        
    
            Prerequisites and co-requisites
        
        
            To successfully study the discipline "Information processing technologies" a student must meet the following requirements:
1. Know:
1. Methods of construction, verification, and research of qualitative characteristics of mathematical models.
2. Principles of building stationary, dynamic, and computer models based on known numerical methods.
2. Be able to:
1. Research the qualitative characteristics of constructed mathematical models.
2. Formulate mathematical optimization problems for such models.
3. Apply mathematical and computer modeling methods to research information processes.
3. Possess:
1. Basic skills in software packages for numerical analysis (MATLAB).
2. In English at a level no lower than Intermediate.
        
    
            Course content
        
        
            Mastering by students of constructive approaches to numerical methods of information processing in various applied applications. Acquaintance of students with the methods of information analysis, its optimal compression, storage or restoration, and creation of software products for this purpose.
        
    
            Recommended or required reading and other learning resources/tools
        
        
            1. I. Parkhomei, N. Tsopa. Osnovy teorii informatsiinykh protsesiv, Chastyna 2. Systemy obrobky syhnaliv, Kyiv, KPI im. Ihoria Sikorskoho, 2020.
2. V.L. Kozhevnykov, A.V. Kozhevnykov. Teoriia informatsii ta koduvannia. Dnipropetrovsk, NHU, 2012.
3. Hansen J.S. GNU Octave Beginner’s Guide, Packt Publishing, 2011.
5. A.I. Nakonechnyi, R.A. Nakonechnyi, V.A. Pavlysh. Tsyfrova obrobka syhnaliv, Lviv, 2010.
        
    
            Planned learning activities and teaching methods
        
        
            Lectures, seminar classes, independent work.
        
    
            Assessment methods and criteria
        
        
            Semester assessment:
The maximum number of points that can be obtained by a student is 100 points.
1. Control work No. 1: RN 1.1, RN 2.1, RN 2.2, RN 4.1 – 30/18 points.
2. Test paper No. 2: RN 2.3, RN 2.4, RN 4.1 – 30/18 points.
3. Current evaluation: RN 1.1, RN 2.1, RN 2.2, RN 2.3, RN 2.4, RN 3.1, RN 3.2, RN 4.1, RN 4.2 – 40/24 points.
Final assessment in the form of credit:
Passing points are defined as the sum of evaluation points for all successfully assessed learning outcomes provided in this program. The minimum threshold level for the total score for all components is 60% of the possible number of points. The student receives an overall positive grade in the discipline if his grade for the semester is at least 60 points.
        
    
            Language of instruction
        
        
            Ukrainian
        
    Lecturers
This discipline is taught by the following teachers
                    Andrii
                    Leonidovych
                    Maksymenko
                
                
                    Complex systems modelling 
Faculty of Computer Science and Cybernetics
            Faculty of Computer Science and Cybernetics
Departments
The following departments are involved in teaching the above discipline
                        Complex systems modelling
                    
                    
                        Faculty of Computer Science and Cybernetics