Institute of Science and Technology · Computer Science and Engineering (%30 English) · Doctorate
Course Objective
The focus of this course is on the theory and application of pattern recognition techniques. Topics covered include machine pattern classification, feature extraction, object recognition, Bayesian decision theory, parametric and non-parametric pattern recognition, supervised and unsupervised pattern recognition, and an overview of these topics is provided.
Course Content
Learning and adaptation, Bayesian decision theory, discriminant functions, parametric techniques, maximum likelihood estimation, Bayesian estimation, adequate statistics, non-parametric techniques, linear discriminant functions, algorithm-independent machine learning, classifiers, unsupervised learning, grouping.
Core Area Distribution
Teaching Methods
Assessment & Evaluation
ECTS / Workload
| Activity | Quantity | Duration (h) | Total Workload |
|---|---|---|---|
| Course Duration (Including Exam Week) | 16 | 3 | 48 |
| Out of Class Study Period | 16 | 3 | 48 |
| Midterm | 1 | 1 | 1 |
| Quiz | 4 | 4 | 16 |
| Assignment | 2 | 4 | 8 |
| Practice | 1 | 3 | 3 |
| Final | 1 | 1 | 1 |
Course Schedule
| Week | Subject | Preparation |
|---|---|---|
| 1 | Pattern Recognition Overview | |
| 2 | Bayesian Decision Theory | |
| 3 | Parametric Models | |
| 4 | Structural and Syntactic Pattern Recognition | |
| 5 | LDA, kNN, SVM | |
| 6 | SVM, BoF algorithms | |
| 7 | Artificial Neural Networks systems | |
| 8 | Performance Evaluation Of Systems | |
| 9 | Proje sunumları | |
| 10 | Proje sunumları | |
| 11 | Proje sunumları | |
| 12 | Proje sunumları | |
| 13 | Proje sunumları | |
| 14 | Proje sunumları | |
| 15 | Proje sunumları | |
| 16 | Proje sunumları |