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.
ELE773 - PATTERN RECOGNITION
|PATTERN RECOGNITION||ELE773||Any Semester/Year||3||0||3||10|
|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, and 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||10||140|
|Presentation / Seminar Preparation||0||0||0|
|Midterms (Study duration)||1||25||25|
|Final Exam (Study duration)||1||30||30|
Matrix Of The Course Learning Outcomes Versus Program Outcomes
|D.9. Key Learning Outcomes||Contrubition level*|
|1. Has highest level of knowledge in certain areas of Electrical and Electronics Engineering.||†||†||†||X||†|
|2. Has knowledge, skills and and competence to develop novel approaches in science and technology.||†||†||†||†||X|
|3. Follows the scientific literature, and the developments in his/her field, critically analyze, synthesize, interpret and apply them effectively in his/her research.||†||†||†||X||†|
|4. Can independently carry out all stages of a novel research project.||†||†||†||X||†|
|5. Designs, plans and manages novel research projects; can lead multidisiplinary projects.||†||X||†||†||†|
|6. Contributes to the science and technology literature.||†||†||†||†||X|
|7. Can present his/her ideas and works in written and oral forms effectively; in Turkish or English.||†||†||X||†||†|
|8. Is aware of his/her social responsibilities, evaluates scientific and technological developments with impartiality and ethical responsibility and disseminates them.||†||X||†||†||†|
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest