Academics > Methods for pattern recognition

Methods for pattern recognition

Type: For the student's choice

Department: applied mathematics

Course description

Course is administrated by Department of Applied Mathematics

Ivan Franko National University of Lviv

 

Study program – Applied Mathematics

Study program is administrated by Faculty of Applied Mathematics and Informatics

 

Indicators Field of knowledge, Speciality,

Academic Degree

 Subject type

(compulsory, optional,  elective)
Number of credits  – 3 Field of knowledge – System Science and Cybernetics Full-time studies
Number of modules – 2 Speciality – 113 Applied Mathematics Elective
Contents modules – 3
Coursework Academic Degree –

Bachelor Degree

 5 year
9 semester
Lectures
Hours per week:

classes – 4

individual work – 5

 32 hours
Practical work
hours
Laboratories
hours
Individual work
80 hours
Final Evaluation: exam

 

Course aim and objectives

Aim. The aim of the course is to get theoretical knowledge and practical skills for students in solving the following tasks: the choice of vocabularies, designing algorithms of recognition and classification, evaluation of the efficiency of coding and recognition processes, and others.

The aim is also to familiarize students with the current state of the problem of recognition and the basic methods of solving problems of pattern recognition. The main idea of ​​the course is to form for students the knowledge that corresponds to both the system and the information approach to the problem of recognition.

Objectives. As a result of studying this discipline, the student should acquire knowledge about modern methods of constructing information models of objects, phenomena and processes, know and be able to use methods of analysis and classification for the implementation of the recognition process.

Learning outcomes

As a result students should

know course of higher mathematics (differential, integral, operational computation, linear algebra, complex variable functions); informatics (programming and algorithmic languages);

be able to apply the studied methods to specific problems.

 

Course outline

Names of contents modules and topics Hours number
Total including
lectures practical laboratories individual
Contents module 1. Recognition systems
10 30
Contents module 2. Mathematical methods in cryptology
10 30
Contents module 3. Public key cryptosystems
4 38
Total hours 30 88

 

Framework of cumulative assessment

Ongoing evaluation and individual work Test Total
Topical Module 1 Topical Module

2

 Topical Module

3

 50 100
Т1 Т2 Т3 Т4 Т5 Т6 Т7 Т8
6 6 6 6 6 6 7 7

Recommended Literature

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  2. Айзерман М.А., Браверман Э.М., Ризонэр Л.И. Метод потенциальных функций в теории обучения машин. – М.: Наука,1970.
  3. Ту Дж, Гонсалес Р. Принципы распознавания образов. -: Мир.1978.
  4. Дуда Р., Харт П. Распознавание образов и анализ сцен.- .:Мир.1976.
  5. Горелик А.Л., Скрипка В.А. Методы распознавания. – М.:Высшая школа.1977.
  6. Дюран Б., Оделл П. Кластерный анализ.-М.:Статистика. 1977.
  7. Тимохин В.И. Применение ЭВМ для расширения задач распознавания образов. – Ленинград: изд-во ЛГУ, 1983.
  8. А.Фор. Восприятие и распознавание образов.- М.: Машиностроение. 1989.
  9. Мандель П.Д. Кластерный анализ. – М.:Финансы и статистика.1988.
  10. Распознавание образов и медицинская диагностика./Под ред. Неймарка Ю.И.- М.:Наука, 1972.