GRADUATE SCHOOL
M.SC. in Computer Engineering (With Thesis)
EEE 502 | Course Introduction and Application Information
| Course Name |
Pattern Recognition
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
EEE 502
|
Fall/Spring
|
3
|
0
|
3
|
7.5
|
| Prerequisites |
None
|
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| Course Language |
English
|
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| Course Type |
Elective
|
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| Course Level |
Second Cycle
|
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| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | - | |||||
| Course Coordinator | ||||||
| Course Lecturer(s) | ||||||
| Assistant(s) | - | |||||
| Course Objectives | This course covers foundations of pattern recognition algorithms and their applications. Topics covered include statistical decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, feature extraction, feature selection, linear classifiers, neural networks, nonmetric methods, unsupervised learning and clustering. |
| Learning Outcomes |
The students who succeeded in this course;
|
| Course Description | Pattern recognition algorithms and their applications, statistical decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, feature extraction, feature selection, linear classifiers, neural networks, nonmetric methods, unsupervised learning and 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 | Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 1) |
| 2 | Bayesian Decision Theory | Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 2) |
| 3 | Maximum Likelihood and Bayesian Estimation | Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 3) |
| 4 | Nonparametric Techniques | Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 4) |
| 5 | Linear Discriminant Functions | Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 5) |
| 6 | Feature Selection and Dimension Reduction Techniques | Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 5) |
| 7 | Multilayer Neural Networks | Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 6) |
| 8 | Midterm | |
| 9 | Multilayer Neural Networks | Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 6) |
| 10 | Radial Basis Function Networks | Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 6) |
| 11 | Stochastic Methods | Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 7) |
| 12 | Nonmetric Methods | Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 8) |
| 13 | Unsupervised Learning and Clustering | Duda, Hart and Stork, Pattern Classification, Wiley,2nd ed., 2001 (Ch. 10) |
| 14 | In-class Presentations | |
| 15 | In-class Presentations | |
| 16 | Review of the Semester |
| Course Notes/Textbooks | The textbook referenced above and course slides |
| Suggested Readings/Materials | Related Research Papers |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation | ||
| Laboratory / Application |
6
|
60
|
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments | ||
| Presentation / Jury | ||
| Project |
2
|
40
|
| Seminar / Workshop | ||
| Oral Exams | ||
| Midterm | ||
| Final Exam | ||
| Total |
| Weighting of Semester Activities on the Final Grade |
8
|
100
|
| Weighting of End-of-Semester Activities on the Final Grade | ||
| 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
|
2
|
32
|
| Study Hours Out of Class |
15
|
4
|
60
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
0
|
||
| Portfolio |
0
|
||
| Homework / Assignments |
0
|
||
| Presentation / Jury |
0
|
||
| Project |
2
|
42
|
84
|
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
0
|
||
| Final Exam |
0
|
||
| Total |
224
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
Program Competencies/Outcomes |
* Contribution Level
|
||||
|
1
|
2
|
3
|
4
|
5
|
||
| 1 | Accesses information in breadth and depth by conducting scientific research in Computer Engineering; evaluates, interprets and applies information. |
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| 2 | Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations. | |||||
| 3 | Uses scientific methods to complete and apply information from uncertain, limited or incomplete data; can combine and use information from different disciplines. |
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| 4 | Is informed about new and upcoming applications in the field and learns them whenever necessary. | |||||
| 5 | Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions. |
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| 6 | Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs | |||||
| 7 | Designs and implements studies based on theory, experiments and modelling; analyses and resolves the complex problems that arise in this process. |
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| 8 | Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations; can work independently and takes responsibility. |
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| 9 | Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale. |
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| 10 | Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form. |
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| 11 | Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications. |
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| 12 | Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity. |
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*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest