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.
4. Selomon D. Szhatie dannyih, izobrazheniy i zvuka, 2004.
5. A.I. Nakonechnyi, R.A. Nakonechnyi, V.A. Pavlysh. Tsyfrova obrobka syhnaliv, Lviv, 2010.
6. Gonsales R.S., Vuds R. Tsifrovaya obrabotka izobrazheniy, 2012.
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