FACULTY OF ENGINEERING
Department of Electrical and Electronics Engineering
CE 322 | Course Introduction and Application Information
Course Name |
Pattern Recognition
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
CE 322
|
Fall/Spring
|
3
|
0
|
3
|
5
|
Prerequisites |
None
|
|||||
Course Language |
English
|
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Course Type |
Elective
|
|||||
Course Level |
First Cycle
|
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Mode of Delivery | - | |||||
Teaching Methods and Techniques of the Course | Problem SolvingApplication: Experiment / Laboratory / WorkshopLecture / Presentation | |||||
Course Coordinator | - | |||||
Course Lecturer(s) | - | |||||
Assistant(s) | - |
Course Objectives | The course focuses on the theory and applications of pattern recognition. The topics include an overview of the problem of pattern classification, feature extraction, object recognition, statistical decision theory, parametric and non-parametric pattern recognition, supervised and unsupervised pattern recognition. |
Learning Outcomes |
The students who succeeded in this course;
|
Course Description | Learning and adoption, Bayesian decision theory, discriminant functions, parametric techniques, maximum likelihood estimation, Bayesian estimation, sufficient statistics, non-parametric techniques, linear discriminants, algorithm independent machine learning, classifiers, unsupervised learning, clustering. |
|
Core Courses | |
Major Area Courses | ||
Supportive Courses | ||
Media and Management Skills Courses | ||
Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
Week | Subjects | Related Preparation |
1 | Introduction to Pattern Recognition, Learning and Adoption | Chapter 1.Sections 1.1-1.6. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
2 | Bayesian Decision Theory | Chapter 2.Sections 2.1-2.4. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
3 | Discriminant Functions | Chapter 2.Sections 2.5,2.6, 2.9. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
4 | Parametric Techniques: Maximum Likelihood Estimation and Bayesian Estimation, Sufficient Statistics | Chapter 3.Sections 3.1-3.7. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
5 | Non-Parametric Techniques | Chapter 4.Sections 4.1-4.4. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
6 | Linear Discriminant Functions | Chapter 5.Sections 5.1-5.8. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
7 | Non-Metric Methods | Chapter 8.Sections 8.1-8.4. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
8 | Midterm Exam | |
9 | Algorithm-Independent Machine Learning | Chapter 9.Sections 9.1-9.3. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
10 | Algorithm-Independent Machine Learning – Resampling | Chapter 9.Sections 9.4,9.5. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
11 | Algorithm-Independent Machine Learning – Classifiers | Chapter 9.Sections 9.6,9.7. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
12 | Unsupervised Learning and Clustering | Chapter 10.Sections 10.1-10.4. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
13 | Unsupervised Learning and Clustering | Chapter 10.Sections 10.5-10.9. Duda, R.O. Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
14 | Project Presentations | |
15 | Semester Review | |
16 | Final Exam |
Course Notes/Textbooks | Duda, R.O.Hart, P.E. and Stork, D.G. Pattern Classification. Wiley-Interscience. 2nd Edition. 2001. |
Suggested Readings/Materials | Bishop, C. M. Pattern Recognition and Machine Learning. Springer. 2007; Marsland, S. Machine Learning: An Algorithmic Perspective. CRC Press. 2009. (Also uses Python.); Theodoridis, S. and Koutroumbas, K. Pattern Recognition. Edition 4. Academic Press, 2008. |
EVALUATION SYSTEM
Semester Activities | Number | Weigthing |
Participation | ||
Laboratory / Application | ||
Field Work | ||
Quizzes / Studio Critiques | ||
Portfolio | ||
Homework / Assignments |
1
|
10
|
Presentation / Jury | ||
Project |
1
|
20
|
Seminar / Workshop | ||
Oral Exams | ||
Midterm |
1
|
30
|
Final Exam |
1
|
40
|
Total |
Weighting of Semester Activities on the Final Grade |
3
|
60
|
Weighting of End-of-Semester Activities on the Final Grade |
1
|
40
|
Total |
ECTS / WORKLOAD TABLE
Semester Activities | Number | Duration (Hours) | Workload |
---|---|---|---|
Theoretical Course Hours (Including exam week: 16 x total hours) |
16
|
3
|
48
|
Laboratory / Application Hours (Including exam week: '.16.' x total hours) |
16
|
0
|
|
Study Hours Out of Class |
14
|
2
|
28
|
Field Work |
0
|
||
Quizzes / Studio Critiques |
0
|
||
Portfolio |
0
|
||
Homework / Assignments |
1
|
10
|
10
|
Presentation / Jury |
0
|
||
Project |
1
|
20
|
20
|
Seminar / Workshop |
0
|
||
Oral Exam |
0
|
||
Midterms |
1
|
20
|
20
|
Final Exam |
1
|
24
|
24
|
Total |
150
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
#
|
Program Competencies/Outcomes |
* Contribution Level
|
||||
1
|
2
|
3
|
4
|
5
|
||
1 | To have adequate knowledge in Mathematics, Science and Electrical and Electronics Engineering; to be able to use theoretical and applied information in these areas on complex engineering problems. |
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2 | To be able to identify, define, formulate, and solve complex Electrical and Electronics Engineering problems; to be able to select and apply proper analysis and modeling methods for this purpose. |
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3 | To be able to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the requirements; to be able to apply modern design methods for this purpose. |
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4 | To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in Electrical and Electronics Engineering applications; uses computer and information technologies effectively. |
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5 | To be able to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or Electrical and Electronics Engineering research topics. |
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6 | To be able to work efficiently in Electrical and Electronics Engineering disciplinary and multi-disciplinary teams; to be able to work individually. |
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7 | To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively, to be able to give and receive clear and comprehensible instructions. |
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8 | To have knowledge about global and social impact of engineering practices on health, environment, and safety; to have knowledge about contemporary issues as they pertain to Electrical and Electronics Engineering; to be aware of the legal ramifications of Electrical and Electronics Engineering solutions. |
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9 | To be aware of ethical behavior, professional and ethical responsibility; to have knowledge about standards utilized in engineering applications
|
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10 | To have knowledge about industrial practices such as project management, risk management, and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development. |
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11 | To be able to collect data in the area of Electrical and Electronics Engineering, and to be able to communicate with colleagues in a foreign language. ("European Language Portfolio Global Scale", Level B1) |
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12 | To be able to speak a second foreign language at a medium level of fluency efficiently. |
|||||
13 | To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Electrical and Electronics Engineering. |
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
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