Pattern Recognition

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 method. Generalized Perceptron Algorithm. Illustration of procedures. Numerical problem solving. Fisher’s linear discriminant analysis (LDA). 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. Classifier Based on Bayes Decision Theory. Karhunen – Loeve transform (principal component analysis). Artificial neural networks. Three- , Multi-layer Perceptrons. Error-backpropagation Learning Algorithm. Hidden Markov Models.

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

Seminars and workshops

Week by Week Schedule

  1. Basic pattern recognition system models and application examples, Perceptron (learning paradigms,Hebbian learning, competitive learning, Boltzmann learning)
  2. Perceptron (learning paradigms,Hebbian learning, competitive learning, Boltzmann learning)
  3. Linear and nonlinear decision functions, Kernel functions (RBF, graph kernels, Mercer kernels, linear kernels)
  4. Discriminant analysis, Fisher linear discriminant analysis (FLDA)
  5. Discriminant analysis
  6. Support vector machine for classification
  7. Support vector machine for classification
  8. Midterm exam
  9. Bayes decision rule for classification
  10. Principal component analysis, Bayes decision rule for classification
  11. Confusion matrix-based performance measures (accuracy, precision, recall, sensitivity, F-score)
  12. Biometric-based person identification
  13. Linguistic approach to pattern recognition, stochastic grammar inference, Markov and hidden Markov models
  14. Cluster analysis, Recognition in multimedia documents (text, speech, audio-video), Markov and hidden Markov models, K-means algorithm, Max-min clustering
  15. Final exam

Study Programmes

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

Literature

(.), S. Theodoridis, K. Koutroumbas, Pattern Recogniton,Elsevier,2009,978-0123695314,
(.), R.O. Duda, P. E. Hart, D.G. Stork, Pattern Classification,J. Wiley, New York, 2001,
(.), L. I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms, Wiley-Blackwell, 2004, 978-0471210788,
(.), L. Gyrgyek, N. Pavešić, S. Ribarić, Uvod u raspoznavanje uzoraka, Tehnička knjiga Zagreb, 1988.,

For students

General

ID 222770
  Winter semester
5 ECTS
L3 English Level
L1 e-Learning
45 Lectures
6 Exercises