ACADEMICS
Course Details
ELE673 - Pattern Recognition
2024-2025 Fall term information
The course is not open this term
ELE673 - Pattern Recognition
| Program | Theoretıcal hours | Practical hours | Local credit | ECTS credit |
| MS | 3 | 0 | 3 | 8 |
| Obligation | : | Elective |
| Prerequisite courses | : | - |
| Concurrent courses | : | - |
| Delivery modes | : | Face-to-Face |
| Learning and teaching strategies | : | Lecture, Question and Answer |
| 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 | : | Know the basic concepts and approaches in pattern recognition, Know the comparative advantages and disadvantages of different approaches, Apply the techniques and algorithms s/he learnt in the class in real-life applications, Propose realistic solutions to previously unencountered pattern recognition problems, Have the adequate knowledge to follow and understand advanced up-to-date pattern recognition algorithms. |
| 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. |
| Weeks | Topics |
|---|---|
| 1 | Basic concepts in pattern recognition |
| 2 | Bayesian decision theory, Error integrals, Minimax and Neyman-Pearson rules |
| 3 | Discriminant functions for the multivariate normal density, Error bounds for normal densities: Chernoff and Bhattacharyya bounds |
| 4 | Bayes decision theory for disrete features, Missing and noisy features |
| 5 | Parameter estimation: Maximum likelihood and Bayes estimation, The notion of sufficient statistic |
| 6 | Problems of dimensionality, Principle component analysis and Fisher linear discriminant analysis |
| 7 | Nonparametric techniques: Parzen windows |
| 8 | Nonparametric techniques: nearest neighbor and k-nearest neighbor algorithms, Common metrics used in pattern recognition |
| 9 | Midterm Exam |
| 10 | Linear discriminant functions and decision regions |
| 11 | Gradient descent methods: Perceptron algorithm, relaxation algorithms |
| 12 | Least squares algorithm, Support Vector machines |
| 13 | Unsupervised learning: Clustering algorithms, k-means clustering, Performance measures in clustering: Minimum variance and scattering criteria |
| 14 | General overview of non-statistical pattern recognition techniques, Decision trees, strings and grammar based methods |
| 15 | Preparation week for final exams |
| 16 | Final exam |
| 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 |
| Quiz | 0 | 0 |
| Midterms | 1 | 25 |
| Final exam | 1 | 40 |
| Total | 100 | |
| Percentage of semester activities contributing grade success | 60 | |
| Percentage of final exam contributing grade success | 40 | |
| Total | 100 | |
| Course activities | Number | Duration (hours) | Total workload |
|---|---|---|---|
| Course Duration | 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, etc.) | 14 | 8 | 112 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework assignment | 7 | 8 | 56 |
| Quiz | 0 | 0 | 0 |
| Midterms (Study duration) | 1 | 10 | 10 |
| Final Exam (Study duration) | 1 | 20 | 20 |
| Total workload | 37 | 49 | 240 |
| Key learning outcomes | Contribution 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. | |||||
| 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. | |||||
| 3. | Follows and interprets scientific literature and uses them efficiently for the solution of engineering problems. | |||||
| 4. | Designs and runs research projects, analyzes and interprets the results. | |||||
| 5. | Designs, plans, and manages high level research projects; leads multidiciplinary projects. | |||||
| 6. | Produces novel solutions for problems. | |||||
| 7. | Can analyze and interpret complex or missing data and use this skill in multidiciplinary projects. | |||||
| 8. | Follows technological developments, improves him/herself , easily adapts to new conditions. | |||||
| 9. | Is aware of ethical, social and environmental impacts of his/her work. | |||||
| 10. | Can present his/her ideas and works in written and oral form effectively; uses English effectively. | |||||
1: Lowest, 2: Low, 3: Average, 4: High, 5: Highest