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FACULTY OF APPLIED MATHEMATICS AND INFORMATICS
SPRING SEMESTER 2016/2017
Row
No.
1
Course Title
Department
Artificial neural
networks. Data mining
process.
Discrete Analysis
and Intelligent
System
Status
Level
(Year)
B (4)
Language
ECTS
Semester
English
1,5
2
Course code / number in the book:
Artificial neural networks. Data mining process.
in plans
Taught by: Nadiya Kolos
Acad. cycle
ECTS credits
Duration
Semester
Contact hours
Bachelor
1.5
8 semester
Spring
86
Year of study
Weekly lectures/seminars
Prerequisites
4-th
2/2
Discrete mathematics, basics of programming and
mathematical modeling
Languages
Examination
Assessment
English, Ukrainian
Test
100-point scale
Aims and objectives: to teach students to create methods and high-performance information technology of
classification based on neural structures for data mining tasks; create models of artificial neural networks with
predetermined properties in selected software environment.
Description: Artificial neural networks one of the basic directions of the modern theory of an artificial intelligence.
In this course we study the structure, properties and applications of artificial neural networks. We consider a lot of
examples, practical problems and computer experiments. In particular, the efficiency of Data Mining algorithms
using artificial neural networks are researched. The neural networks and its learning algorithms for classification
and the cluster analyses are developed.
Reading list:
1. Kevin Gurney. An introduction of neural networks. — UCL Press, London, 1997.
2. Deboeck G., Kohonen T, Visual Explorations in Finance and Investments Using Self-organizing Maps,
Springer-Verlag, 1998.
3. Руденко О. Г., Бодянський Є. В., Штучні нейронні мережі, Харків, 2006.
4. Горбань П.А.,Технология извлечения знаний из нейронных сетей: апробация, проектирование ПО,
использование в психолингвистике, Омск, 2002.
5. А.В. Сивохин, А.А. Лушников, С.В. Шибанов, Искусственные нейронные сети. Лабораторный
практикум, Пенза 2004.