Study

Computing

Introduction to Pattern Recognition

  • Class 45
  • Practice 0
  • Independent work 75
Total 120

Course title

Introduction to Pattern Recognition

Lecture type

Elective

Course code

183489

Semester

6

ECTS

4

Lecturers and associates

Course objectives

Basic pattern recognition system models and application examples.
Linear and nonlinear decision functions.
Linear and nonlinear decision functions.
Linear and nonlinear decision functions.
Perceptron (learning paradigms,Hebbian learning, competitive learning, Boltzmann learning).
Feature extraction and coding.
Bayes decision rule for classification.
Midterm exam.
Bayes decision rule for classification.
Multivariate Gaussian Bayes model.
Linguistic approach to pattern recognition, stochastic grammar inference.
Linguistic approach to pattern recognition, stochastic grammar inference.
K-means algorithm.
Adaptive clustering algorithms (ISODATA).
Final exam.

Required reading

(.), S. Theodoridis, K. Koutroumbas, Pattern Recogniton,
(.), R.O. Duda, P. E. Hart, D.G. Stork, Pattern Classification,
(.), L. Gyrgyek, N. Pavešić, S. Ribarić, Uvod u raspoznavanje uzoraka,
(.), J.T. Tou, R.C. Gonzalez, Pattern Recognition Principles, Addison-Wesley,1977

Minimal learning outcomes

  • Understanding basic concepts of pattern recognition
  • Apply the knowledge in pattern recognition system design
  • Integrate and combine knowledge for obtaining the new solutions
  • Evaluate and assess usefulness of pattern recognition methods
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