Neural networks

Course: Economic analysis and statistics

Structural unit: Faculty of Economics

Title
Neural networks
Code
3.2.1.5
Module type
Вибіркова дисципліна для ОП
Educational cycle
Second
Year of study when the component is delivered
2023/2024
Semester/trimester when the component is delivered
3 Semester
Number of ECTS credits allocated
5
Learning outcomes
PLO1. Formulate, analyze and synthesize solutions to scientific and practical problems PLO8. Collect, process and analyze statistical data, scientific and analytical materials needed to solve complex economic problems PLO10. Apply modern information technologies and specialized software in socio-economic research and management of socio-economic systems PLO15. Plan and perform scientific and / or applied research, make sound conclusions based on research results, present results, argue your opinion
Form of study
Full-time form
Prerequisites and co-requisites
1. Know: the theory of methods of statistical analysis (disciplines: Statistics, Statistics for economists). 2. Possess: programming methods (disciplines: Fundamentals of programming, programming, Piton and others).
Course content
The curriculum consists of one module: Neural networks and their capabilities. Direct propagation neural network, Backpropagation algorithm, Convolutional neural network, recurrent neural networks, Kohonen network, Pseudo-inverse associative neural network, data preparation for neural networks, examples of neural networks for image classification, audio signals, digital signals, natural language.
Recommended or required reading and other learning resources/tools
Planned learning activities and teaching methods
The purpose of the discipline - is to master the basic methods of working with neural networks. The educational task of the course is to study the basic types and capabilities of neural networks.
Assessment methods and criteria
Forms of student assessment: - semester assessment of 75 points maximum / 45 points minimum: 1. Participation in laboratory classes (speeches, practical exercises) - 15 points / 9 points 2. 2 tests - 20 points / 12 points 3. Performing independent work - 20 points / 12 points - final assessment in the form of a test
Language of instruction
Ukrainian

Lecturers

This discipline is taught by the following teachers

Oleksandr Serhiiovych Podskrebko
Department of Economic Cybernetics
Faculty of Economics

Departments

The following departments are involved in teaching the above discipline

Department of Economic Cybernetics
Faculty of Economics