Infopaket

Intelligent data processing

ДВС.3.05

Вибіркова дисципліна для ОП

First

2023/2024

7 Semester

4

PRN12. Be able to apply methods and algorithms of intelligent data analysis for the tasks of classification, forecasting, cluster analysis, finding associative rules using software tools to support multidimensional data analysis based on the use of DataMining, TextMining, WebMining technologies. PRN19.3. Know the algorithms of information analysis and be able to apply them in solving practical problems. PRN21.3. Know artificial intelligence technologies and be able to apply them in solving practical problems.

Know: discrete mathematics, mathematical logic, theory of algorithms, data structures and algorithms, programming paradigms, and programming fundamentals within the scope of standard university courses. Be able to: apply knowledge from the above disciplines to solving problems, be able to write programs in a high-level language. Possess: basic skills of using logico-mathematical symbols and basics of programming

It is taught in the 7th semester of the 4th year in the amount of 120 hours. (4 ECTS credits), in particular: lectures – 42 hours, consultations – 2 hours, independent work – 76 hours. During the course of study, students should master the main methods and models of intelligent data analysis and the means of their implementation, in particular, master the algorithms for processing streaming data, learn to analyze and avoid modern problems related to the collection and processing of information. The acquired knowledge will allow you to effectively build software products that comprehensively use analytical, information and communication technologies for data processing and analysis.

The main ones: 1. Marchenko O.O. Current Issues in Data Mining: Study Guide for Students Faculty of Computer Sciences and Cybernetics / O.O. Marchenko, T.V. Rossada — Kyiv, 2017. 2. Aivazyan S. A. Applied statistics: Dependence research: Ref. ed. / S. A. Ayvazyan, I. S. Enyukov, L. D. Meshalkin. — M.: Finances and Statistics, 1985. 3. Aivazyan S. A. Applied statistics: Classification and dimensionality reduction: Ref. ed. / S.A. Ayvazyan, V.M. Bukhstaber, I. S. Enyukov, L. D. Meshalkin. — M.: Finance and statistics, 1989. 4. Shitykov V.K., Classification, regression and other algorithms of Data Mining p using R / V.K. Shitykov, S.E. Mastytskyi - Electronic book, access address: https://github.com/ranalytics/data-mining. , 2017. 5. Shlesinger M. Ten lectures on statistical and structural recognition./ M. Shlesinger, V. Hlavach. - Kyiv.: Naukova dumka, 2004. ..

Lectures, consultations, independent work

- semester assessment: 1. Control work 1 (according to part 1): РН1.1, РН1.2, РН2.1, – 20 points/ 12 points. 2. Control work 2 (according to part 2): RN 1.1., RN1.2, RN2.1, RN 3.1 – 20 points/ 12 points. 3. Practical task according to part 2 (software implementation of the algorithm from part 2 with application to a set of test data, obtaining a numerical result and drawing up a report): RN 1.1., RN1.2, RN2.1, RN 3.1, RN4.1, RN4. 2 — 20 points/ 12 points.

Ukrainian