Artificial Intelligence in Computing Technologies

Major: Information management systems and technologies
Code of subject: 7.122.01.O.008
Credits: 5.00
Department: Publishing Information Technologies
Lecturer: Dr.Sc, Prof., Roman Tkachenko
Semester: 2 семестр
Mode of study: денна
Learning outcomes: • have a basic knowledge of neurophysiology, which is the basis of building technical analogues of biological neurons; • know the main features of neuralparadyhms and possible areas of practical application; principles of learning and adaptation of systems; • be able to carry out formulation and solution of problems anticipation, recognition, compression, forecasting; • know the possibility of using ANN in economics, business and industry.
Required prior and related subjects: Prerequisites: • Calculus; • Artificial intelligence; • Linear algebra; • Analytical Geometry.
Summary of the subject: Architecture hardware and software neuro-computers. The principles of adaptation and learning. Models of biological and technical neurons. Feedforward artificial neural networks. Perceptrons. Feedback neural networks. Neural Fuzzy networks. The model of geometric transformations. ANN with deep learning.
Assessment methods and criteria: • written reports on laboratory work, oral examination, reference work (40%); • final control (control measure, exam): written, oral form (60%)
Recommended books: 1. Ткаченко Р. Моделювання методами нейронних мереж /Р.О. Ткаченко, П.Р. Ткаченко, Н.О. Мельник: навч.-методичний посібник: ЛІБС УБС НБУ. – Львів, 2010. – 114 с. 2. Руденко О. Штучні нейронні мережі:Навч. посібник / О.Г. Руденко, Є.В. Бодянський. – Харків: ТОВ «Компанія СМІТ, 2006. – 404 с. 3. Хайкин С. Нейронные сети: полный курс: пер. с англ. / Саймон Хайкин. – М.: Вильямс, 2006. – 1106 с.