Shape recognition

1.

Subject title

Shape recognition

Препознавање на облици

2.

Code

m23_s_027

3.

Study program

Cloud Computing, IT management, Bioinformatics, Security, Cryptography and Coding, Eco-informatics, Inteligent Systems, Internet Technologies and cyber security, Computer Science, Statistics and Data Analytics, Software for embedded systems, IT management, Bioinformatics, Security, Cryptography and Coding, Statistics and Data Analytics, Еducation with ICT, Cloud Computing, Data science in computer science and engineering, Software Engineering, Software Engineering,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Втор циклус

6.

Academic year / semester

5 / Летен

7. Number of ECTS credits

6.0

8.

Instructor

проф. д-р Дејан Ѓорѓевиќ проф. д-р Ѓорѓи Маџаров

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

The purpose of the course is to get acquainted with the basics of modern techniques in the field of form recognition and sampling classification. Upon completion of the course candidates: will have deepened knowledge of advanced technologies and methods of form recognition; will be able to understand, analyze and formulate general problems in the field of form recognition; They will be able to successfully apply algorithms for form analysis and recognition when solving real problems; They will be able to conceive, analyze, implement and assess the performance of a sampling system.

11.

Subject content:

Machine perception. Statistical decision -making theory. Baes`s decision -making theory. Optimal decisions, classification, probable distributions. Dimensionality, capacity of clamps, model selection, training, evaluation, complexity. Parametric approach to learning. Basic statistical techniques, moving and variance; estimate of density, regression and analysis of discriminant. Non -parametric techniques, methods of closest neighbor, flexible metrics. Linear Discriminant Functions, Fisher`s Classifier, Neuronal Networks and Machines with Long Vectors as Classifiers. Non -metrical methods, deciding trees. Markova Verigi, application of hidden Markov model for classification. Using the context in recognizing forms. Stochastic methods, genetic algorithms. Error estimation, empirical error criteria, confidentiality interval. Extraction of features, analysis of main components, selection of inclusions of features. Bagging, boosting, combining classifiers. Design, analysis, implementation and application of form recognition algorithms. Practical application, text recognition, handwriting, speech. Scene analysis, robotic vision.

12.

Learning methods:

Предавања поддржани со презентации преку слајдови, интерактивни предавања, вежби (користење на опрема и софтверски пакети), тимска работа, пример случаи, поканети гости предавачи, самостојна изработка и одбрана на проектна задача и семинарска работа, учење во електронско опкружување (форуми, консултации).

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

60 + 0 + 45 + 45 + 30 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

60 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

0 hours

16.

Other forms of activities

16.1.

Project tasks

45 hours

16.2.

Independent tasks

45 hours

16.3.

Homework

30 hours

17.

Grading method

17.1.

Tests

0 points

17.2.

Seminar work / project (presentation: written and oral)

45 points

17.3.

Activities and learning

10 points

17.4.

Final exam

0 points

18.

Grading criteria (points / grade)

up to 50 points

5 (five) (F)

from 51 to 60 points

6 (six) (E)

from 61 to 70 points

7 (seven) (D)

from 71 to 80 points

8 (eight) (C)

from 81 to 90 points

9 (nine) (B)

from 91 to 100 points

10 (ten) (A)

19.

Condition for signature and taking final exam

реализирани активности

20.

Language of instruction

македонски и англиски

21.

Quality assurance method

механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature