Course Details

ELE773 - Pattern Recognition

2023-2024 Summer term information
The course is not open this term
ELE773 - Pattern Recognition
Program Theoretýcal hours Practical hours Local credit ECTS credit
PhD 3 0 3 10
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, and 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
Assessment Methods
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
Workload and ECTS Calculation
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 10 140
Presentation / Seminar Preparation 0 0 0
Project 0 0 0
Homework assignment 7 9 63
Quiz 0 0 0
Midterms (Study duration) 1 25 25
Final Exam (Study duration) 1 30 30
Total workload 37 77 300
Matrix Of The Course Learning Outcomes Versus Program Outcomes
Key learning outcomes Contribution level
1 2 3 4 5
1. Has highest level of knowledge in certain areas of Electrical and Electronics Engineering.
2. Has knowledge, skills and and competence to develop novel approaches in science and technology.
3. Follows the scientific literature, and the developments in his/her field, critically analyze, synthesize, interpret and apply them effectively in his/her research.
4. Can independently carry out all stages of a novel research project.
5. Designs, plans and manages novel research projects; can lead multidisiplinary projects.
6. Contributes to the science and technology literature.
7. Can present his/her ideas and works in written and oral forms effectively; in Turkish or English.
8. Is aware of his/her social responsibilities, evaluates scientific and technological developments with impartiality and ethical responsibility and disseminates them.
1: Lowest, 2: Low, 3: Average, 4: High, 5: Highest
General Information | Course & Exam Schedules | Real-time Course & Classroom Status
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