Introduction to Pattern Recognition

Learning Outcomes

  1. understanding basic concepts of pattern recognition
  2. apply the knowledge in pattern recognition system design
  3. integrate and combine knowledge for obtaining the new solutions
  4. evaluate and assess usefulness of pattern recognition methods

Forms of Teaching

Lectures

Partial e-learning

Week by Week Schedule

  1. Basic pattern recognition system models and application examples.
  2. Linear and nonlinear decision functions.
  3. Linear and nonlinear decision functions.
  4. Linear and nonlinear decision functions.
  5. Perceptron (learning paradigms,Hebbian learning, competitive learning, Boltzmann learning).
  6. Feature extraction and coding.
  7. Bayes decision rule for classification.
  8. Midterm exam.
  9. Bayes decision rule for classification.
  10. Multivariate Gaussian Bayes model.
  11. Linguistic approach to pattern recognition, stochastic grammar inference.
  12. Linguistic approach to pattern recognition, stochastic grammar inference.
  13. K-means algorithm.
  14. Adaptive clustering algorithms (ISODATA).
  15. Final exam.

Study Programmes

University undergraduate
Computing (study)
Elective Courses (6. semester)
Electrical Engineering and Information Technology (study)
Elective Courses (6. semester)

Literature

(.), 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,

General

ID 183489
  Summer semester
4 ECTS
L3 English Level
L1 e-Learning
45 Lectures
0 Exercises
0 Laboratory exercises
0 Project laboratory