| 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. |
Course Outline Weekly
| 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 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| 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. | | | | | |
