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| ISTL7063 | Statistical Pattern Recognition | 3+0+0 | ECTS:7.5 | | Year / Semester | Fall Semester | | Level of Course | Third Cycle | | Status | Elective | | Department | DEPARTMENT of STATISTICS and COMPUTER SCIENCES | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Uğur ŞEVİK | | Co-Lecturer | Asst. Prof. Uğur ŞEVİK | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | This course will examine various statistical models and popular pattern recognition algorithms. Two complementary approaches will be used for discrimination of data. Calculating the probability density function, Bayes theorem approach and discriminant function approach will be presented. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Be able to comprehend the fundamental principles and notions of statistical pattern recognition systems. | 1 - 2 | 1,3,5, | | PO - 2 : | Be able to decide the most suitable classifier for a given classification problem | 1 - 2 | 1,3,5, | | PO - 3 : | Be able to extract discriminatory features from datasets | 1 - 2 | 1,3,5, | | PO - 4 : | Be able to understand the principles of supervised and unsupervised learning, and generalization ability. | 1 - 2 | 1,3,5, | | PO - 5 : | Be able to design and evaluate the performance of statistical pattern recognition system for a given task. | 1 - 2 | 1,3,5, | | PO - 6 : | Be able to code the algorithms on a computer. | 1 - 2 | 1,3,5, | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| The topics of this course are Bayesian Classifiers, Maximum Likelihood Estimation, Hidden Markov Models, Bayesian Parameter Estimation, K-Nearest Neighbor Classifier, Linear Separators, Multilayer Neural Networks, Support Vector Machines, K-Means Clustering and Feature Selection.
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction to Statistical Pattern Recognition | | | Week 2 | Density Estimation - Parametric | | | Week 3 | Density Estimation - Bayesian | | | Week 4 | Density Estimation ? Non-Parametric (k-NN, Histogram Based Approaches) | | | Week 5 | Density Estimation ? Non-Parametric (Kernel Methods ) | | | Week 6 | Linear Discriminant Analysis | | | Week 7 | Non-Linear Discriminant Analysis ( Radial Basis Functions) | | | Week 8 | Non-Linear Discriminant Analysis ( Non-Linear SVM) | | | Week 9 | MIDTERM | | | Week 10 | Rule and Decision Tree Induction | | | Week 11 | Classifier Combination Methods | | | Week 12 | Performance Assessment | | | Week 13 | Feature Extraction | | | Week 14 | Feature Selection | | | Week 15 | Clustering | | | Week 16 | FINAL EXAM | | | |
| 1 | Statistical Pattern Recognition, A. Webb, 3rd. Edition, Wiley, 2011. | | | 2 | Pattern Classification: R.O. Duda, P.E. Hart, D.G. Stork 2. Baskı, Wiley, 2000. | | | |
| 1 | Introduction to Statistical Pattern Recognition, K. Fukunaga, Academic Press, 1990. | | | 2 | Pattern Recognition, S. Theodoridis, K. Koutroumbas, 3rd edition, Academic Press, 2006. | | | 3 | Introduction to Machine Learning: E. Alpaydın, MIT Press, 2004. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 8 | 18/11/2025 | 2 | 30 | | Quiz | 12 | 09/12/2025 | 2 | 20 | | End-of-term exam | 16 | 06/01/2026 | 2 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 8 | 14 | 112 | | Arasınav için hazırlık | 6 | 1 | 6 | | Arasınav | 2 | 1 | 2 | | Dönem sonu sınavı için hazırlık | 10 | 1 | 10 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 174 |
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