Adaptive information processing and recognition
Course: Applied mathematics
Structural unit: Faculty of Computer Science and Cybernetics
Title
Adaptive information processing and recognition
Code
ВК.2.03
Module type
Вибіркова дисципліна для ОП
Educational cycle
Second
Year of study when the component is delivered
2023/2024
Semester/trimester when the component is delivered
4 Semester
Number of ECTS credits allocated
3
Learning outcomes
PLO11.2. Understand the main areas of applied mathematics and computer science to the extent necessary for the development of general professional mathematical disciplines, applied disciplines, and the use of their methods in the chosen profession. PLO15.2. Be able to implement automatic and automated systems using mathematical and computer models, and developed algorithms.
Form of study
Full-time form
Prerequisites and co-requisites
1. Know the materials of standard courses in mathematical analysis, linear algebra, discrete mathematics, differential equations, operations research, probability theory, mathematical statistics, mathematical physics, numerical methods, and decision-making theories.
2. Be able to build and investigate models of decision-making and prediction of the behavior of objects modeled by stochastic processes based on decision-making theory.
3. Possess elementary skills in building decision-making models.
Course content
Familiarization and mastery of the basic principles of researching models for predicting the behavior of the studied objects using the example of financial market assets; acquisition of practical decision-making skills in various fields of activity. Formation of competence in the practical application of mathematical models for predicting the behavior of objects of arbitrary nature. The course includes two content parts and two tests. The discipline ends with an exam.
Recommended or required reading and other learning resources/tools
1. John.C.Hull. Options, futures and other derivatives.
2. Christopher Hunter. Derivative Securities.
3. Patrick S. Hagan. Continuous time stochastic processes.
4. Bernt Oksendal. Stochastic Differential Equation.
5. Martin Baxter, Andrew Rennie. Financial calculus. An introduction to derivative. Press syndicate of the University of Cambridge.
6. Tomasz R Beiletcki, Marek Rutkowski. Credit risk: modeling, valuation and Hedging.
7. Rama Cont, Peter Tankov. Financial modeling with jump processes. Capman and Hall/ CRC Press company, London, New York. Washington.
8. Behun V.V., Horbunov O.V., Kadenko I.M., Pysmennyi E.M., ta in. Imovirnisnyi analiz bezpeky VSTO. Kyiv, 2000. Hlava 1, p. 12-28.
9. NUREG/CR – 6116. Version 7.0. 2008, Systems Analysis Programs for Hands – on Integrated Reliability Evaluations (SAPHIRE). Idaho, 2008.
Planned learning activities and teaching methods
Lectures, seminars, independent work.
Assessment methods and criteria
The maximum number of points that a student can receive is 60 points.
1. Test No. 1: 20/12 points.
2. Test No. 2: 20/12 points.
3. Oral answers: 20/12 points.
Final assessment (in the form of an exam):
The maximum number of points that a student can receive is 40 points.
Form of conduct: written work. Types of tasks: 4 written tasks (1 theoretical question and 1 practical task for each content part).
A student receives a positive grade for the discipline if his exam grade is at least 24 points. A student is admitted to the exam if during the semester he scored at least 36 points in total, and completed and passed two tests on time.
Language of instruction
Ukrainian
Lecturers
This discipline is taught by the following teachers
Oleksander
Pokutnyi
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