Infopaket

Data Mining & Artifical Intellince

ВК 2.3

Вибіркова дисципліна для ОП

Second

2023/2024

3 Semester

4

Apply their knowledge to identify and solve problems and make informed decisions in the thematic processing of geological, geophysical and other geospatial data. Be able to communicate with experts and experts of different levels of other fields of knowledge, including in the international context, in the global information environment. Plan and carry out scientific experiments, write scientific papers in the field of geoinformatics. Be able to carry out thematic processing and interpretation of geospatial data obtained by different methods of research of the geological environment, to develop appropriate algorithms and software products, create geodatabases, create web publications of cartographic data. Demonstrate the ability to adapt and act in a new situation related to work in the profession, the ability to generate new ideas in the field of geoinformatics. Create specialized software products, use web technologies and remote sensing data.

Full-time form

In order to better master the educational material of the discipline, students must have knowledge and skills in the field of computer science, geophysics, Earth physics, computer technology, work with spreadsheets, databases, speak English.

The basics of data mining implementation in modern decision support systems, organization of data warehouses, implementation of operational analytical data processing (OLAP), identification of patterns in data by solving problems of clustering, classification, regression, time series forecasting, use knowledge-oriented (knowledge representation models and logical inference) and connectionist (artificial neural networks) approaches to artificial intelligence to data mining.

1. Deductor. Analyst Guide. Version 5.3. - BaseGroup™ Labs, 1995-2013. 2. Methods and models of data analysis: OLAP and Data Mining / A. A. Barseghyan, M. S. Kupriyanov, V. V. Stepanenko et al. - 2nd ed., revised. and additional - St. Petersburg. : BHV-Petersburg, 2004. 3. Analysis of data and processes: textbook / A. A. Barseghyan, M. S. Kupriyanov, I. I. Kholod, M. D. Tess, S. I. Elizarov. - St. Petersburg: BHV-Petersburg. 2009. 4. Bondarev V.N., Ade F.G. Artificial intelligence: Proc. allowance for universities. - Sevastolpol: SevNTU Publishing House, 2002. 5. Bratko I. Algorithms of artificial intelligence in the PROLOG language. - M.: Williams Publishing House, 2004. 6. V.P. Borovikov. STATISTICS. The art of data analysis on a computer: For professionals - St. Petersburg: Peter, 2003.

Lectures, practical classes, consultations, independent work

The control is carried out according to the module-rating system and provides: performance of 7 practical works (where students have to demonstrate the quality of the acquired knowledge and solve the tasks using the methods and tools outlined by the teacher), and conducting 2 written modular tests. The final assessment is conducted in the form of a written and oral exam

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