Timing data are obtained using weekly schedule program tables. To make sure whether the course is cancelled or time-shifted for a specific week one should consult the supervisor and/or follow the announcements.
Course definition tables are extracted from the ECTS Course Catalog web site of Hacettepe University (http://akts.hacettepe.edu.tr) in real-time and displayed here. Please check the appropriate page on the original site against any technical problems. Course data last updated on 24/03/2020.
ELE673 - PATTERN RECOGNITION
|PATTERN RECOGNITION||ELE673||Any Semester/Year||3||0||3||8|
|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.|
|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
|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|
|Specific practical training||0||0|
|Percentage of semester activities contributing grade succes||0||60|
|Percentage of final exam contributing grade succes||0||40|
Workload and ECTS calculation
|Activities||Number||Duration (hour)||Total Work Load|
|Course Duration (x14)||14||3||42|
|Specific practical training||0||0||0|
|Study Hours Out of Class (Preliminary work, reinforcement, ect)||14||8||112|
|Presentation / Seminar Preparation||0||0||0|
|Midterms (Study duration)||1||10||10|
|Final Exam (Study duration)||1||20||20|
Matrix Of The Course Learning Outcomes Versus Program Outcomes
|D.9. Key Learning Outcomes||Contrubition level*|
|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