Neural networks and artificial intelligence

Course: Neuropsychology

Structural unit: Faculty of Psychology

Title
Neural networks and artificial intelligence
Code
ДВС.1.04
Module type
Вибіркова дисципліна для ОП
Educational cycle
Second
Year of study when the component is delivered
2021/2022
Semester/trimester when the component is delivered
1 Semester
Number of ECTS credits allocated
4
Learning outcomes
LO1. To search, process and analyze professionally important knowledge from various sources using modern information and communication technologies. LO 3. Summarize empirical data and formulate theoretical conclusions.LO 11. Evaluate achievements and determine and argue the prospects of their own scientific work.
Form of study
Full-time form
Prerequisites and co-requisites
1. To know the basics of general psychology, psychology of consciousness, CNS anatomy and physiology of the ANS, basic methods of scientific research. 2. Possess skills of scientific research, search, processing and analysis of information from various sources, use of information and communication technologies.
Course content
The purpose of the discipline is to acquire the necessary knowledge in the field of artificial intelligence, to learn to distinguish the differences between natural and artificial neural networks, to improve learning approaches by studying the features of machine learning.
Recommended or required reading and other learning resources/tools
1. Rudenko O. H., Bodianskyi Ye. V. Shtuchni neironni merezhi: Navchalnyi posibnyk. – Kharkiv: TOV "Kompaniia SMIT", 2006. 2. Hlybovets M.M., Oletskyi O.V. Shtuchnyi intelekt. K.: VD ″Kyievo-Mohylianska akademiia″, 2002. 3. Anisimov A.V., Hlybovets M.M., Kravchenko I.V., Oletskyi O.V., Tereshchenko V.M., Kuliabko P.P. Systemy shtuchnoho intelektu. // Navchalnyi posibnyk, VPTs "Kyivskyi universytet" 2000. 4. Churchland, P. M., 2007. Neurophilosophy at Work, Cambridge: Cambridge University Press. 5. Churchland, P. S., 2002. Brain-wise: Studies in Neurophilosophy, Cambridge, MA: MIT Press. 6. Russell, S., & Norvig, P., 2009. Artificial Intelligence: A Modern Approach, Upper Saddle River, NJ: Prentice Hall.
Planned learning activities and teaching methods
Lecture, seminar, practical class, individual work
Assessment methods and criteria
Assessment methods: oral answers (presentation at the seminar, participation in discussions and discussions, supplementary comments), report with or without presentation, practical tasks, final control work. The form of final control is credit. Organization of assessment: All types of work are evaluated during the semester. of work. The maximum number of points for work during the semester is 100. The grade is based on the results of the student's work during the semester. Students who have scored a total of less than 60 points during the semester, but but more than the critical-calculated minimum of 40 points, pass the test. A passing grade is worth a maximum of 20 points. Students who have scored a total of less than the critical and minimum of 40 points are not allowed to take the test. The recommended minimum for admission to the test is 40 points. Rating scale: 0-59 - not credited; 60-100 - passed.
Language of instruction
Ukrainian

Lecturers

This discipline is taught by the following teachers

Olena - Komar
Department of Philosophy and Methodology of Science
Faculty of Philosophy

Departments

The following departments are involved in teaching the above discipline

Department of Philosophy and Methodology of Science
Faculty of Philosophy