ACADEMICS
Course Detail

ELE 673 Pattern Recognition
2016-2017 Spring term information

The course is not open this term

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://ects.hacettepe.edu.tr) in real-time and displayed here. Please check the appropriate page on the original site against any technical problems.

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
Prerequisite(s)
Course languageTurkish
Course typeElective 
Mode of DeliveryFace-to-Face 
Learning and teaching strategiesLecture
Question and Answer
 
Instructor (s)Assist. Prof. Dr. A.Semih Bingöl 
Course objectiveIn 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
  1. Know the basic concepts and approaches in pattern recognition,
  2. Know the comparative advantages and disadvantages of different approaches,
  3. Apply the techniques and algorithms s/he learnt in the class in real-life applications,
  4. Propose realistic solutions to previously unencountered pattern recognition problems,
  5. Have the adequate knowledge to follow and understand advanced up-to-date pattern recognition algorithms.
Course ContentBasics 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.
 
ReferencesDuda 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

WeeksTopics
Week 1Basic concepts in pattern recognition
Week 2Bayesian decision theory, Error integrals, Minimax and Neyman-Pearson rules
Week 3Discriminant functions for the multivariate normal density, Error bounds for normal densities: Chernoff and Bhattacharyya bounds
Week 4Bayes decision theory for disrete features, Missing and noisy features
Week 5Parameter estimation: Maximum likelihood and Bayes estimation, The notion of sufficient statistic
Week 6Problems of dimensionality, Principle component analysis and Fisher linear discriminant analysis
Week 7Nonparametric techniques: Parzen windows
Week 8Nonparametric techniques: nearest neighbor and k-nearest neighbor algorithms, Common metrics used in pattern recognition
Week 9Midterm Exam
Week 10Linear discriminant functions and decision regions
Week 11Gradient descent methods: Perceptron algorithm, relaxation algorithms
Week 12Least squares algorithm, Support Vector machines
Week 13Unsupervised learning: Clustering algorithms, k-means clustering, Performance measures in clustering: Minimum variance and scattering criteria
Week 14General overview of non-statistical pattern recognition techniques, Decision trees, strings and grammar based methods
Week 15Preparation week for final exams
Week 16Final exam

Assesment methods

Course activitiesNumberPercentage
Attendance00
Laboratory00
Application00
Field activities00
Specific practical training00
Assignments735
Presentation00
Project00
Seminar00
Midterms125
Final exam140
Total100
Percentage of semester activities contributing grade succes060
Percentage of final exam contributing grade succes040
Total100

Workload and ECTS calculation

Activities Number Duration (hour) Total Work Load
Course Duration (x14) 14 3 42
Laboratory 0 0 0
Application000
Specific practical training000
Field activities000
Study Hours Out of Class (Preliminary work, reinforcement, ect)148112
Presentation / Seminar Preparation000
Project000
Homework assignment7856
Midterms (Study duration)11010
Final Exam (Study duration) 12020
Total Workload3749203

Matrix Of The Course Learning Outcomes Versus Program Outcomes

D.9. Key Learning OutcomesContrubition level*
12345
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

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