Subject
Pattern Recognition
| 1. | Course Title |
Pattern Recognition Shape recognition |
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| 2. | Code | m23_s_027 | ||||||||||||
| 3. | Study Programme | — | ||||||||||||
| 4. | Organizer of the study programme (unit, institute, department or division) | Faculty of Computer Science and Engineering | ||||||||||||
| 5. | Degree level (first, second, third cycle) | Second cycle | ||||||||||||
| 6. | Academic year / semester | 10 / Summer | ||||||||||||
| 7. | Number of ECTS credits | 6 | ||||||||||||
| 8. | Teacher | Dejan Djordjevic, Georgi Madjarov | ||||||||||||
| 9. | Prerequisites for enrolling in the course | — | ||||||||||||
| 10. | Objectives of the course programme (competences) | The goal of the course is for students to become familiar with the fundamentals of modern techniques in the field of pattern recognition and sample classification. Upon completion of the course, candidates will have in-depth knowledge of advanced technologies and methods for pattern recognition; will be able to understand, analyze, and formulate general problems in the field of pattern recognition; will be able to successfully apply algorithms for pattern analysis and recognition to solve real-world problems; will be able to design, analyze, implement, and evaluate the performance of a pattern recognition system. | ||||||||||||
| 11. | Course content | Machine perception. Theory of statistical decision making. Bayesian decision theory. Optimal decisions, classification, probability distributions. Dimensionality, classifier capacity, model selection, training, evaluation, complexity. Parametric approach to learning. Basic statistical techniques, displacement and variance; density estimation, regression and discriminant analysis. Nonparametric techniques, nearest neighbor methods, flexible metrics. Linear discriminant functions, Fisher's classifier, neural networks and support vector machines as classifiers. Nonmetric methods, decision trees. Markov chains, application of hidden Markov model for classification. Using context in pattern recognition. Stochastic methods, genetic algorithms. Error estimation, empirical error criteria, confidence interval. Feature extraction, principal component analysis, feature subset selection. Bagging, boosting, classifier combination. Design, analysis, implementation, and application of pattern recognition algorithms. Practical applications, text recognition, handwriting recognition, speech recognition. Scene analysis, robotic vision. |
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| 12. | Learning methods | Lectures supported by slide presentations, interactive lectures, practical classes (using equipment and software packages), teamwork, case studies, guest lecturers, independent preparation and defence of a project assignment and seminar paper, and learning in an electronic environment (forums and consultations). | ||||||||||||
| 13. | Total available time | 6 ECTS x 30 hours = 180 hours | ||||||||||||
| 14. | Distribution of available time | 60 + 0 + 45 + 45 + 30 = 180 hours | ||||||||||||
| 15. | Forms of teaching activities |
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| 16. | Other forms of activities |
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| 17. | Assessment method |
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| 18. | Grading criteria (points / grade) |
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| 19. | Requirement for obtaining a signature and taking the final exam | completed activities | ||||||||||||
| 20. | Language of instruction | Macedonian and English | ||||||||||||
| 21. | Method for monitoring the quality of teaching | internal evaluation and survey mechanism | ||||||||||||
| 22. | Literature |
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