Introduction to pattern recognition

1.

Subject title

Introduction to pattern recognition

Вовед во препознавање на облици

2.

Code

F23L3W089

3.

Study program

Примена на информациски технологии, Софтверско инженерство и информациски системи, Компјутерски науки, Компјутерско инженерство, Интернет, мрежи и безбедност, Информатичка едукација, Software engineering and information systems, Примена на информациски технологии, Софтверско инженерство и информациски системи, Компјутерски науки, Компјутерско инженерство, Интернет, мрежи и безбедност, Software engineering and information systems, Стручни студии за програмирање, Стручни студии за програмирање, 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)

Прв циклус

6.

Academic year / semester

4 / Зимски

7. Number of ECTS credits

6.0

8.

Instructor

проф. д-р Дејан Ѓорѓевиќ

9.

Prerequisites for enrollment

Вештачка интелигенција или Вовед во науката за податоци или Машинско учење

10.

Subject goals and competencies:

The aim of the course is for students to learn the main concepts of the methods and techniques applied to recognition of shapes. After completing the course, candidates will be able to design, realization and implementation of systems for automatic recognition of shapes, their assessment performance and their optimization.

11.

Subject content:

Lectures: 1. Introduction to the problem of shape recognition. 2. Machine perception, components of a pattern recognition system. 3. Classifiers based on Bayesian decision theory, Linear classifiers. 4. Types of landmarks, feature extraction, feature selection. 5. Machine learning for pattern recognition. Supervised, unsupervised learning, semi-supervised learning, calcifier performance evaluation 6. Non-parametric classification methods, kNN classifier, implementation, kd trees. 7. Non-metric classification methods, decision trees, training, pruning. 8. Large margin classifiers, kernel techniques, Support vector machines. 9. Bias/Variance decomposition, ensembles of classifiers, feature fusion 10. Neural networks and deep architectures for pattern recognition 11. Unsupervised learning, clustering 12. Application in real systems for recognizing shapes and specifics Exercises: 1. Tools for data analysis and pattern recognition 2. Libraries for data analysis and pattern recognition 3. Tasks - Bayesian classifier, linear classifier, logistic regression 4. Tasks - feature extraction, feature transformation, feature selection 5. Procedure for supervised learning and assessment and evaluation of performance and metrics 6. Tasks with kNN classifier and kd trees 7. Tasks with decision trees and knowledge extraction 8. Problems with machines with support vectors 9. Tasks ensembles of classifiers and feature fusion 10. Neural network tasks and defining network architectures 11. Tasks from unsupervised learning 12. Implementation of real shape recognition systems

12.

Learning methods:

предавања, аудиториски вежби, лабораториски вежби, проектни задачи, домашни задачи

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

30 + 45 + 15 + 15 + 75 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

30 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

45 hours

16.

Other forms of activities

16.1.

Project tasks

15 hours

16.2.

Independent tasks

15 hours

16.3.

Homework

75 hours

17.

Grading method

17.1.

Tests

10 points

17.2.

Seminar work / project (presentation: written and oral)

15 points

17.3.

Activities and learning

10 points

17.4.

Final exam

70 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 15, 16

20.

Language of instruction

македонски и англиски

21.

Quality assurance method

интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature