Infopaket

Mathematical methods of information processing

ВК.4.03.02

Вибіркова дисципліна для ОП

First

2023/2024

5 Semester

3

PRN1. Apply knowledge of the basic forms and laws of abstract thinking, the basics of the methodology of scientific knowledge, the forms and methods of extracting, analyzing, processing and synthesizing information in the subject area of ​​computer science. PRN2. To use the modern mathematical apparatus of continuous and discrete analysis, linear algebra, analytical geometry, in professional activities to solve problems of a theoretical and applied nature in the process of designing and implementing informatization objects. PRN3. Demonstrate knowledge of patterns of random phenomena, their properties and operations on them, models of random processes and modern software environments for solving problems of statistical processing of experimental data and construction of predictive models.

– Know: basic concepts and methods of probability theory and mathematical statistics, mathematical analysis and algebra, fundamentals of programming. – Be able to: apply knowledge of probability theory and mathematical statistics, develop programs at the basic level. - Have elementary skills: solve problems in probability theory and mathematical statistics, programming skills.

The discipline has the following sections: Data pre-processing. Correlation analysis. Regression analysis. Analysis of variance. Covariance analysis. Time series analysis. Classification problems. Factor analysis. The discipline is a discipline of the student's free choice. Uses concepts from probability theory and mathematical statistics, mathematical analysis and algebra. Acts as a base for disciplines: basics of pattern recognition, intelligent data processing, problems of artificial intelligence, neural networks and neurocomputing, pattern recognition and scene analysis, the basics of Data Mining, a number of disciplines of the student's free choice (by blocks), and will also be useful when writing bachelor's and master's graduation theses. Taught in the 5th semester, volume 90 hours (3 ECTS credits), of which lectures – 28 hours, practical – 14 hours, consultations – 2 hours, independent work - 46 hours 6 laboratory works and assessment are provided.

Osnovnі: 1) Karimov R. N. Osnovy diskriminantnogo analiza: Uchebno-metodicheskoe posobie. — Saratov: SGTU, 2002. -108st. 2) Lagutin M.B., Nagliadnaia matematicheskaia statistika, M.: BINOM, 2007. 3) Maiboroda R.Є. Komp’iuterna statistika – profesіinii start, 2018, 482 st. http://probability.univ.kiev.ua/userfiles/mre/compsta1.pdf 4) Maiboroda R.Є., Sugakova O.V. Analіz danikh za dopomogoiu paketa R http://matphys.rpd.univ.kiev.ua/downloads/courses/mmatstat/Statistics_with_R.pdf 5) Slabospits-kii O.S., Analіz danikh. Poperednia obrobka, VPТs “Kiїvs-kii unіversitet” (2001). 6) Slabospits-kii O.S., Osnovi koreliatsіinogo analіzu danikh, VPТs “Kiїvs-kii unіversitet” (2006). 7) Slabospits-kii O.S., Osnovi dispersіinogo analіzu danikh, VPТs “Kiїvs-kii unіversitet” (2006). ..

Lectures, practical classes, consultations, independent work

- semester assessment: 1. Laboratory work 1 + thematic test: RN 1., RN 2.1, RN 2.2 – 18 points. 2. Laboratory work 2 + thematic test: RN 1., RN 2.1, RN 2.2 – 12 points. 3. Laboratory work 3 + thematic test: RN 1., RN 2.1, RN 2.2 – 18 points. 4. Laboratory work 4 + thematic test: RN 1., RN 2.1, RN 2.2 – 16 points. 5. Laboratory work 5 + thematic test: RN 1., RN 2.1, RN 2.2 – 18 points. 6. Laboratory work 6 + thematic test: RN 1., RN 2.1, RN 2.2 – 18 points. The maximum number of points that can be obtained by a student: 100 points. To receive an overall positive grade in the discipline, the number of points scored by the student during the academic semester must be at least 60.

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