Neural network technologies

Course: Data science

Structural unit: Faculty of information Technology

Title
Neural network technologies
Code
ВК 1.5
Module type
Вибіркова дисципліна для ОП
Educational cycle
First
Year of study when the component is delivered
2022/2023
Semester/trimester when the component is delivered
6 Semester
Number of ECTS credits allocated
5
Learning outcomes
Apply models of knowledge representation in modern information systems, process audio, video and text information and numerical data, including using neural network technologies.
Form of study
Prerequisites and co-requisites
Be able to perform problem analysis for structuring input and output information. Have the skills to work in any modern instrumental programming environment, develop programs in high-level languages to implement the given task.
Course content
While studying the discipline, students acquire knowledge of the theoretical and practical foundations of neural network technologies; study the methods of designing and training neural networks; gain practical experience in the basics of programming artificial neural networks based on the use of modern software tools. The discipline is aimed at forming in students the ability to design and develop software implementations of artificial neural networks for the tasks of analysis and processing of various information, decision-making, pattern recognition based on the use of existing neural network paradigms, neural network learning algorithms.
Recommended or required reading and other learning resources/tools
1. Subbotin S. O. Neural networks: theory and practice: teaching. manual / S. O. Subbotin. - Zhytomyr: Vyd.O. O. Evenok, 2020. – 184 p. 2. Trotsky V.V. Methods of artificial intelligence: educational, methodological and practical guide. - Kyiv: University of Economics and Law "KROK", 2020 - 86 p. 3. Francois Chollet Deep Learning with Python, Second Edition - Manning Publications, 2021. – 386 p.
Planned learning activities and teaching methods
Lectures, Laboratory classes, Student's work independently
Assessment methods and criteria
Student assessment is carried out from all types of work, including the study of theoretical material, laboratory work, and individual tasks, modular control works. The maximum number of points that a student can receive for work during the semester is 60 points on a 100-point scale. Summative assessment is an exam in written form. The overall score for the exam is 40 points on a 100-point scale. If a student receives less than 24 points during the exam, they get an "unsatisfactory" grade and the earned points are not counted. The recommended minimum for admission to the exam is 36 points, the critically calculated minimum is 20 points. In order to be admitted to the exam, it is mandatory to complete all laboratory work and receive a positive assessment from the semester modular control work.
Language of instruction
Ukrainian

Lecturers

This discipline is taught by the following teachers

Departments

The following departments are involved in teaching the above discipline