ELE673 - PATTERN RECOGNITION
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| PATTERN RECOGNITION | ELE673 | Any Semester/Year | 3 | 0 | 3 | 8 |
| Prequisites | ||||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Question and Answer | |||||
| Instructor (s) | Department Faculty | |||||
| Course objective | In order to equip the students with the capability to solve real-life problems in pattern recognition, this course aims to teach the following topics to the students: ? basic concepts in pattern recognition, ? basics of statistical decision theory, ? parametric and nonparametric approaches and their differences, ? other techniques used in moders pattern recognition systems, while mainly staying in the context of statistical techniques. | |||||
| Learning outcomes |
| |||||
| Course Content | Basics of pattern recognition: Pattern classes, features, feature extraction, classification. Statistical decision theory, Bayes classifier, Minimax and Neyman-Pearson rules, error bounds. Supervised learning: Probability density function estimation, maximum likelihood and Bayes estimation. Nonparametric pattern reconition techniques: Parzen windows, nearest neighbor and k-nearest neigbor algorithms. Discriminant analysis, least squares and relaxation algorithms. Unsupervised learning and clustering. Other approaches to pattern recognition. | |||||
| References | Duda R. O., Hart P. E., and Stork D. G., Pattern Classification, 2nd ed., John Wiley and Sons, 2001. Webb A., Statistical pattern recognition, Oxford University Press Inc., 1999. Theodoridis S., Koutroumbas K., Pattern recognition, Academic Press, 1999. | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Basic concepts in pattern recognition |
| Week 2 | Bayesian decision theory, Error integrals, Minimax and Neyman-Pearson rules |
| Week 3 | Discriminant functions for the multivariate normal density, Error bounds for normal densities: Chernoff and Bhattacharyya bounds |
| Week 4 | Bayes decision theory for disrete features, Missing and noisy features |
| Week 5 | Parameter estimation: Maximum likelihood and Bayes estimation, The notion of sufficient statistic |
| Week 6 | Problems of dimensionality, Principle component analysis and Fisher linear discriminant analysis |
| Week 7 | Nonparametric techniques: Parzen windows |
| Week 8 | Nonparametric techniques: nearest neighbor and k-nearest neighbor algorithms, Common metrics used in pattern recognition |
| Week 9 | Midterm Exam |
| Week 10 | Linear discriminant functions and decision regions |
| Week 11 | Gradient descent methods: Perceptron algorithm, relaxation algorithms |
| Week 12 | Least squares algorithm, Support Vector machines |
| Week 13 | Unsupervised learning: Clustering algorithms, k-means clustering, Performance measures in clustering: Minimum variance and scattering criteria |
| Week 14 | General overview of non-statistical pattern recognition techniques, Decision trees, strings and grammar based methods |
| Week 15 | Preparation week for final exams |
| Week 16 | Final exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 7 | 35 |
| Presentation | 0 | 0 |
| Project | 0 | 0 |
| Seminar | 0 | 0 |
| Midterms | 1 | 25 |
| Final exam | 1 | 40 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 0 | 60 |
| Percentage of final exam contributing grade succes | 0 | 40 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 14 | 8 | 112 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework assignment | 7 | 8 | 56 |
| Midterms (Study duration) | 1 | 10 | 10 |
| Final Exam (Study duration) | 1 | 20 | 20 |
| Total Workload | 37 | 49 | 240 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Has general and detailed knowledge in certain areas of Electrical and Electronics Engineering in addition to the required fundamental knowledge. | X | ||||
| 2. Solves complex engineering problems which require high level of analysis and synthesis skills using theoretical and experimental knowledge in mathematics, sciences and Electrical and Electronics Engineering. | X | ||||
| 3. Follows and interprets scientific literature and uses them efficiently for the solution of engineering problems. | X | ||||
| 4. Designs and runs research projects, analyzes and interprets the results. | X | ||||
| 5. Designs, plans, and manages high level research projects; leads multidiciplinary projects. | X | ||||
| 6. Produces novel solutions for problems. | X | ||||
| 7. Can analyze and interpret complex or missing data and use this skill in multidiciplinary projects. | X | ||||
| 8. Follows technological developments, improves him/herself , easily adapts to new conditions. | X | ||||
| 9. Is aware of ethical, social and environmental impacts of his/her work. | X | ||||
| 10. Can present his/her ideas and works in written and oral form effectively; uses English effectively | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest