Advanced course of Analysis and Probability Theory
Course: Applied Mathematics
Structural unit: Faculty of Computer Science and Cybernetics
            Title
        
        
            Advanced course of Analysis and Probability Theory
        
    
            Code
        
        
            ДВС.3.02
        
    
            Module type 
        
        
            Вибіркова дисципліна для ОП
        
    
            Educational cycle
        
        
            First
        
    
            Year of study when the component is delivered
        
        
            2022/2023
        
    
            Semester/trimester when the component is delivered
        
        
            6 Semester
        
    
            Number of ECTS credits allocated
        
        
            5
        
    
            Learning outcomes
        
        
            PLO21.3. Understand the fundamental areas of mathematics and computer science, to the extent necessary for learning mathematical disciplines, applied disciplines and using their methods in a chosen profession.
PLO22.3. Understand the main areas of mathematical logic, theory of algorithms and computational theory, programming theory, probability theory and mathematical statistics.
PLO23.3. Be able to use professional knowledge, skills and abilities in the field of fundamental sections of mathematics and computer science for research of real processes of different nature.
PLO24.3. Be able to independently analyze the relevant subject area, be able to develop mathematical and structural algorithmic models.
        
    
            Form of study
        
        
            Full-time form
        
    
            Prerequisites and co-requisites
        
        
            To successfully learn the discipline Advanced course of Analysis and Probability Theory the student should satisfy the following requirements. 
They know (a) fundamentals  of mathematical methods for construction, verification and investigation of qualitative characteristics of deterministic and stochastic mathematical models; (b) classical methods of Linear Algebra, Probability Theory, Calculus and Functional Analysis.
They can (a) investigate qualitative characteristics of available mathematical models; (b) apply classical methods for solving applied problems in deterministic and stochastic models; (c) analуze the nature and goals of construction of mathematical structures and models.
They should be able to (a) apply classical methods of Linear Algebra, Probability Theory,  Calculus and Functional Analysis; (b) seek information in open sources and properly analyze it. 
        
    
            Course content
        
        
            The discipline is aimed at learning basic results of Renewal Theory for random walks with nonegative steps, the Operator Algebras theory and methods of the proofs and mastering technical tools  which are intrinsic to this subject area. The subject matter includes classical theorems of Renewal Theorem like the elementary renewal theorem, Blackwell’s theorem, the key renewal theorem, the strong law of large numbers for the number of renewals; spectral theory, commutative Banach and C*-algebras, realisation of abstract C*-algebras by Hilbert space operators. The present course is a natural continuation of the disciplines “Calculus”, “Probability Theory”, “Linear Algebra” and “Functional Analysis”.
        
    
            Recommended or required reading and other learning resources/tools
        
        
            1. Iksanov O.M. Elements of renewal theory, with applications: Electronic lecture notes. -2023.-122 p. https://do.csc.knu.ua/wp-content/uploads/2023/09/LN_renewal.pdf
2.Iksanov A. Renewal theory for perturbed random walks and similar processes. Cham: Birkhauser, 2016.-250 p.
3. Mitov K.V., Omey E. Renewal processes. Cham: Springer, 2014. -122 p.
4.Gut A. Stopped random walks: Limit theorems and applications. 2nd edition. New York: Springer-Verlag, 2009.—263 p.
5. Wegge-Olsen N.E. K-Theory and C*-algebras: a friendly approach. New-York: Oxford Science Publications. The Clarendon Press, Oxford University Press, 1993. – 370 p.
6. Hille E., Phillips R.S. Functional analysis and semigroups. Providence: AMS, 1974.—808 p.
7. Conway J.W.. A Course in Functional Analysis. Second edition. Graduate Texts in Mathematics, 96. New York: Springer-Verlag,1990. – 399 p.
        
    
            Planned learning activities and teaching methods
        
        
            Lectures, consultations, test works, independent work. 
        
    
            Assessment methods and criteria
        
        
            Intermediate assessment:
The maximal number of available points is 60.
Test work no. 1: 30/18 points.
Test work no. 2: 30/18 points.
Final assessment (in the form of exam): 
The maximal number of available points is 40.
The form of exam: writing.
The types of assignments are 4 writing assignments (2 theoretical and 2 practical).
        
    
            Language of instruction
        
        
            Ukrainian
        
    Lecturers
This discipline is taught by the following teachers
                    Alexander
                    M.
                    Iksanov
                
                
                    Operations Research  
Faculty of Computer Science and Cybernetics
            Faculty of Computer Science and Cybernetics
Departments
The following departments are involved in teaching the above discipline
                        Operations Research 
                    
                    
                        Faculty of Computer Science and Cybernetics