Introduction to Pattern Recognition

Course Description

Pattern recognition. Basic motivation. Pattern recognition model. Examples of pattern recognition systems. Relation: artificial intelligence ? pattern recognition. Feature extraction and selection. Linear and non-linear transformations. Feature coding. Linear decision functions. Non-linear decision functions. Learning procedures for decision functions. Statistical classification. Bayes classifier. Estimation of parameters.Non-numerical pattern recognition. Structural classification. Syntactic recognition. Stochastic Grammars and Languages. Cluster analysis. Examples of pattern recognition system design.

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

Lectures followed by numerous solutions to problems

Exercises

Examples of numerical solutions of problems

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
2. Mid Term Exam: Written 50 % 50 % 50 %
Final Exam: Written 50 % 50 %

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
Elective Courses (6. semester)
Elective Courses (6. semester)
[FER2-HR] Computer Science - module
Elective Courses (6. semester)
[FER2-HR] Information Processing - module
Elective Courses (6. semester)
[FER2-HR] Software Engineering and Information Systems - module
Elective Courses (6. semester)

Literature

Konstantinos Koutroumbas, Sergios Theodoridis (2008.), Pattern Recognition, Academic Press
(.), 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,

Associate Lecturers

For students

General

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

Grading System

89 Excellent
74 Very Good
61 Good
50 Sufficient