Infopaket

Data Mining & Artificial Intelligence

ВК.2.02

Вибіркова дисципліна для ОП

Second

2024/2025

3 Semester

4

1.1 Place and role of data mining discipline, main tasks and application areas 1.2 Architecture and purpose of Decision Support Systems (DSS) and OLTP 1.3 Architecture and purpose of data warehouses and OLAP in DSS 1.4 Knowledge representation models and logical inference in knowledge-oriented AI 1.5 Methods of clustering, classification, regression, time series forecasting 1.6 Architecture, training methods and implementation of artificial neural networks in connectionist AI 2.1 Use advantages of data mining approaches in various domains 2.2 Formulate clustering, classification, regression, forecasting tasks and select suitable methods 2.3 Apply neural network models for clustering, classification, regression, forecasting tasks 3.1 Efficient use of software environments and active communication with teacher and peers 4.1 Ability to independently set data mining tasks and responsibly implement them

Students must have knowledge and skills in informatics, Earth physics, computer technology, spreadsheets, databases, and English language.

Basics of implementing Data Mining principles in modern DSS, data warehouses, OLAP; methods of clustering, classification, regression, time series forecasting; knowledge-oriented and connectionist (neural networks) approaches in AI for data analysis.

Hladun A.Ya., Rohushyna Yu.V. Data Mining: Knowledge Discovery in Data. Kyiv: ADEF-Ukraine, 2016. Vinnychuk O.Yu., Vinnychuk I.S. Intelligent Data Analysis: Laboratory Practicum. Chernivtsi: Chernivtsi Univ., 2014. Lishchyna N.M. Data Mining: Lecture Notes. Lutsk: LNTU, 2016. Kussul N.M., Shelestov A.Yu., Lavreniuk A.M. Intelligent Computing. Kyiv: Naukova Dumka, 2006. Oliinyk A.O., Subbotin S.O., Oliinyk O.O. Intelligent Data Analysis. Zaporizhzhia: ZNTU, 2012. The R Project for Statistical Computing – Manual (https://cran.r-project.org/manuals.html ) Spiegelhalter D. The Art of Statistics: How to Learn from Data. Basic Books, 2021. Claybrook B.G. OLTP: Online Transaction Processing Systems. Wiley, 1992.

Lectures – 24 hours; practical classes – 14 hours; consultations – 2 hours; independent work – 80 hours.

– Modular test 1 – 12 points – Modular test 2 – 12 points – Practical works – 36 points Final exam (written-oral) – 40 points Evaluation on a 100-point scale (min. 60, max. 100).

Ukrainian (English)