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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.