Pattern recognition and scene analysis
Course: Informatics
Structural unit: Faculty of Computer Science and Cybernetics
Title
Pattern recognition and scene analysis
Code
ДВС.3.09
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
8 Semester
Number of ECTS credits allocated
4
Learning outcomes
PRN22.3. Know artificial intelligence technologies and be able to apply them in solving practical problems.
PRN23.3. Know the technologies of implementation of "human-computer" interaction and be able to apply them.
Form of study
Prerequisites and co-requisites
1. Know the basic concepts of general algebra, computational mathematics, probability theory, mathematical statistics, matrix theory, data analysis, theory of formal languages and grammars.
2. To be able to effectively apply the specified mathematical apparatus to solve various practical problems.
Course content
The subject of the educational discipline "Pattern recognition and scene analysis" is a review of the main concepts, models, results and methods of the modern mathematical theory of pattern recognition and scene analysis.
It is taught in the 8th semester of the 4th course in the amount of 120 hours.
4 ECTS credits, in particular: lectures - 40 hours, independent work - 78 hours, consultations - 2 hours.
The course includes 2 tests.
The discipline ends with an exam in the 8th semester.
Recommended or required reading and other learning resources/tools
Osnovnі:
1. Duda R., Khart P. Raspoznavanie obrazov i analiz stsen. – M.: Mir, 1976
2. Tu Dzh., Gonsales R. Printsipy raspoznavaniia obrazov. – M.: Mir, 1978
3. Fu K.S. Strukturnye metody v raspoznavanii obrazov. – M.: Mir, 1977
4. Fu K.S. Syntactіc Pattern Recognіtіon and Applіcatіons. – N.Y.: Prentіce-Hall, 1982
5. Vasil-ev V.I. Raspoznaiushchie sistemy. Spravochnik. – K.: Nauk.dumka, 1983
6. Shlezinger M., Glavach V. Desiat- lektsii po statisticheskomu i strukturnomu raspoznavaniiu.
– K.: Nauk.dumka, 2004
7. Trokhimchuk R. N. Metod postroeniia avtomatov, realizuiushchikh zadannoe mnozhestvo
eksperimentov.– Kibernetika, 1975, # 1, s.90-93
..
Planned learning activities and teaching methods
Lectures, consultations, independent work
Assessment methods and criteria
Student evaluation forms: Results of students' educational activities
are evaluated on a 100-point scale. When assigning points, the following is taken into account: the assessment for the test
work - 20 points, students' work at seminars - 10 points, independent work - 30 points.
The final control is conducted in the form of an exam - 40 points.
The final score is 100 = 60 + 40.
If the student was absent from writing the test for valid reasons, which are documented, he has the right to one retake with the possibility of receiving the maximum number of points. The deadline for rewriting is determined by the teacher.
A student is admitted to the exam if he scored at least 20 points in the semester. To receive an overall positive grade in the discipline, the grade for the exam must be at least 24 points.
The exam is considered failed if the total number of points in the discipline is less than 60 points.
Language of instruction
Ukrainian
Lecturers
This discipline is taught by the following teachers
Departments
The following departments are involved in teaching the above discipline