Pattern Recognition

Data is displayed for academic year: 2023./2024.

Lecturers

Assoc. Prof.
Zoran Kalafatić

Lectures

Prof.
Slobodan Ribarić

Course Description

Task of pattern recognition. Features, Feature vectors, Classifier. Pattern Recognition System Model. Determining linear decision functions. Gradient learning procedures. Perceptron algorithm with fixed correction. Variants of perceptron algorithm. Ho-Kashyap algorithm. Generalized Perceptron Algorithm. Illustration of procedures. Numerical problem solving. Fisher’s linear discriminant analysis (FLDA). Multiple discriminant analysis. Learning and testing sample sets – methods of evaluation. Illustration of procedures. Numerical problem solving. Support Vector Machines (SVM). Nonlinear Classifiers. Generalized Linear Decision Functions. Dichotomy. Kernel Based Methods. Karhunen – Loeve transform (principal component analysis). Artificial neural networks. Three- , Multi-layer Perceptrons. Error-backpropagation Learning Algorithm.

Study Programmes

University graduate
[FER3-HR] Audio Technologies and Electroacoustics - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Communication and Space Technologies - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Computational Modelling in Engineering - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Computer Engineering - profile
Elective Courses (1. semester)
[FER3-HR] Computer Science - profile
Core-elective courses (1. semester) (3. semester)
[FER3-HR] Control Systems and Robotics - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Data Science - profile
Elective Courses (1. semester) (3. semester)
Elective Courses of the Profile (1. semester) (3. semester)
[FER3-HR] Electrical Power Engineering - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Electric Machines, Drives and Automation - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Electronic and Computer Engineering - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Electronics - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Information and Communication Engineering - profile
Elective Courses (1. semester) (3. semester)
Elective Courses of the Profile (1. semester)
Elective Coursesof the Profile (3. semester)
[FER3-HR] Network Science - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Software Engineering and Information Systems - profile
Elective Course of the profile (3. semester)
Elective Course of the Profile (1. semester)
Elective Courses (1. semester) (3. semester)
[FER2-HR] Computer Science - profile
Theoretical Course (1. semester)
[FER2-HR] Information Processing - profile
Theoretical Course (1. semester)

Learning Outcomes

  1. explain and define concepts of pattern recognition
  2. explain and distinguish porocedures, methods and algorithms related to pattern recognition
  3. apply methods from the pattern recognition for new complex applications
  4. analyze and breakdown problem related to the complex pattern recognition system
  5. design and develop a pattern recognition system for the specific application
  6. evaluate quality of solution of the pattern recognition system

Forms of Teaching

Lectures

lectures are performed in combination with numerical solving of examples

Seminars and workshops

presentations of students’ projects

Independent assignments

team student projects

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Mid Term Exam: Written 50 % 50 % 0 %
Final Exam: Written 50 % 50 %
Comment:

Attendance at lectures is mandatory and controlled - students who have an attendance of less than 80% CANNOT ATTEND the midterm exam and final exam (as part of the continuous examination). All they have left is the "classic" exam.

Week by Week Schedule

  1. Task of pattern recognition. Features, Feature vectors, Classifier. Basic pattern recognition system models and application examples, Perceptron (learning paradigms, Hebbian learning, competitive learning, Boltzmann learning)
  2. Pattern recognition system models.Perceptron (learning paradigms,Hebbian learning, competitive learning, Boltzmann learning)
  3. Linear and nonlinear decision functions
  4. Gradient learning procedures
  5. Variations of perceptron algorithm. Ho-Kashyap algorithm. Generalized Perceptron Algorithm
  6. Fisher’s linear discriminant analysis (FLDA)
  7. Multiple discriminant analysis
  8. Midterm exam
  9. Support Vector Machines (SVM) Kernel functions (RBF, graph kernels, Mercer kernels, linear kernels)
  10. Nonlinear Classifiers. Generalized Linear Decision Functions. Dichotomy. Kernel Based Methods. Kernel functions (RBF, graph kernels, Mercer kernels, linear kernels) Principal component analysis, Bayes decision rule for classification
  11. Nonlinear Classifiers. Generalized Linear Decision Functions. Dichotomy. Kernel Based Methods. Kernel functions (RBF, graph kernels, Mercer kernels, linear kernels)
  12. Nonlinear Classifiers. Generalized Linear Decision Functions. Dichotomy. Kernel Based Methods. Kernel functions (RBF, graph kernels, Mercer kernels, linear kernels) Performance System Evaluatiom Confusion matrix-based performance measures (accuracy, precision, recall, sensitivity, F-score) Biometric-based person identification
  13. Karhunen – Loeve transform / Principal Component Analysis
  14. Artificial neural networks. Three- , Multi-layer Perceptrons. Error-backpropagation Learning Algorithm.
  15. Final exam

Literature

S. Theodoridis, K. Koutroumbas (2009.), Pattern Recogniton, Elsevier, Academic Press
R.O. Duda, P. E. Hart, D.G. Stork (2001.), Pattern Classification, J. Wiley
L. I. Kuncheva (2014.), Combining Pattern Classifiers: Methods and Algorithms,, Wiley-Blackwell
L. Gyrgyek, N. Pavešić, S. Ribarić (1988.), Uvod u raspoznavanje uzoraka, Tehnička knjiga Zagreb,

For students

General

ID 222770
  Winter semester
5 ECTS
L1 English Level
L1 e-Learning
45 Lectures
0 Seminar
6 Exercises
0 Laboratory exercises
0 Project laboratory
0 Physical education excercises

Grading System

89 Excellent
74 Very Good
61 Good
50 Sufficient

Similar Courses