Design of expert systems and decision support systems
Course: Computer science
Structural unit: Faculty of information Technology
Title
Design of expert systems and decision support systems
Code
ВК 2.7
Module type
Вибіркова дисципліна для ОП
Educational cycle
First
Year of study when the component is delivered
2021/2022
Semester/trimester when the component is delivered
7 Semester
Number of ECTS credits allocated
5
Learning outcomes
Use tools for the development of client-server applications, design conceptual, logical and physical models of databases, develop and optimize queries to them, create distributed databases, data stores and showcases, knowledge bases, including on cloud services, using web languages -programming;
Apply modern technologies of automation of analysis, design and development of complex objects and systems at various stages of their life cycle;
Use knowledge and practical skills in the architecture and technology of developing modern intelligent software systems.
Form of study
Prerequisites and co-requisites
Know the basics of decision-making theory, discrete mathematics, methods and technologies of algorithmization and programming. Be able to perform an analysis of the information space of tasks in order to structure input and output information, information that circulates in the system and is needed to solve specific tasks. Possess elementary skills of working in any instrumental programming environment, develop programs in high-level languages to implement the given task.
Course content
Within the framework of the discipline "Design of expert systems and decision-making support systems" the classification of decision-making problems, some features of information, expert evaluation, some aspects of decision-making, characteristics and features of CSDP, stages of CSDP design, design of CSDP architecture, design of the user interface of CSDP, general information about expert systems, rule-based expert systems, expert system design, expert system life cycle.
The goal of the discipline is to provide students with theoretical knowledge and practical skills in designing expert systems and decision support systems using the latest scientific research and modern software.
Recommended or required reading and other learning resources/tools
Planned learning activities and teaching methods
Lectures, laboratory work, independent work
Assessment methods and criteria
The level of achievement of all planned learning outcomes is determined by the results of written test papers, performance of laboratory and independent works. Assessment of students is carried out during the semester for all types of work. The total score for the semester is formed as a weighted sum of points earned by the student for various types of work. The maximum number of points that a student can receive for work in a semester does not exceed 100 points. Total score = test papers (30%) + laboratory papers (50%) + independent papers (20%). The credit is issued to the student based on the results of work during the semester. If the resulting total number of points is obtained from 60 and above, the student is considered enrolled. If the student wishes to improve his result, if he has credit points, he has the right to take a credit for which 20 points are awarded, but the total number of points cannot be more than 100 points.
Language of instruction
Ukrainian
Lecturers
This discipline is taught by the following teachers
Faculty of information Technology
Departments
The following departments are involved in teaching the above discipline
Faculty of information Technology