Infopaket

Stochastic algorithms

ВК.4.04.03

Вибіркова дисципліна для ОП

First

2022/2023

6 Semester

3

PLO 4. Use methods of computational intelligence, machine learning, neural network and fuzzy data processing, genetic and evolutionary programming to solve problems of recognition, forecasting, classification, identification of control objects, etc.

Full-time form

Know: discrete mathematics, data structures and algorithms, probability theory and mathematical statistics in the volume of standard university courses. Be able to: apply knowledge from the above disciplines to solving problems. Possess elementary skills: working with a computer

The discipline is a selective component of the training of specialists at the first (bachelor's) level of higher education in the field of knowledge 12 "Information technologies" from the specialty 122 "Computer science", educational and professional program "Informatics". It is taught in the 6th semester, volume 90 hours (3 ECTS credits), of which lectures – 28 hours, laboratory work – 14 hours, consultations – 2 hours, independent work - 46 hours The discipline is based on the basic concepts of statistical modeling and methods stochastic algorithms, principles of their implementation in programming languages, application in applied tasks. Stochastic algorithms, solving educational and practical problems are considered.

Osnovnі: 1. Iu.V. Kozachenko, A.O. Pashko (2016) Tochnіst- modeliuvannia vipadkovikh protsesіv v rіvnomіrnіi metritsі. 2. S.M. Ermakov (2009). Metod Monte-Karlo v vychislitel-noi matematike. Vvodnyi kurs. - Nevskii dialekt, SPb. 3. Olive Ibe (2009). Markov Processes for Stochastic Modeling. Academic Press. 4. Pierre Bremaud (1998). Markov Chains, Givvs Fields, Monte Carlo Simulation, and Queues. Springer. 5. Handbook of Simulation. Principles, Methodology, Advances, Applications, and Practice (1998), J. Banks (editor), Wiley, NY. 6. G.S. Fishman (1999), Monte Carlo. Concepts, algorithms and applications, Springer-Verlag, New York-Berlin-Amsterdam. ..

Lectures, laboratory work, consultations, independent work

- semester assessment: Sixth semester 1. Control work 1: РН1.1, РН1.2, РН2.1, РН2.2 – 20 points/12 points. 2. Practical task according to part 2 (software implementation of the algorithm from part 2 of application to a set of test data, obtaining a numerical result and compiling report): RN 1.1., RN1.2, RN2.1, RN 3.1, RN4.1, RN4.2 — 20 points/ 12 points. - final evaluation (in the form of credit): - the maximum number of points that can be obtained by a student: 40; - learning outcomes that are evaluated: PH1.1, PH1.2, PH2.1, PH3.1; - form of conduct: written; - types of tasks: task (40%), theoretical question (60%). A student is admitted to the exam if he scored at least 20 points in the semester.

Ukrainian