GRADUATE SCHOOL
M.SC. in Bioengineering (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 | To be able to have adequate knowledge in Mathematics, Life Sciences and Bioengineering; to be able to use theoretical and applied information in these areas to model and solve Bioengineering problems. |
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| 2 | To be able to use scientific methods to complete and apply information from uncertain, limited or incomplete data; to be able to combine and use information from related disciplines. |
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| 3 | To be able to design and apply theoretical, experimental and model-based research; to be able to solve complex problems in such processes. |
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| 4 | Being able to utilize Natural Sciences and Bioengineering principles to design systems, devices and processes. |
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| 5 | To be able to follow and apply new developments and technologies in the field of Bioengineering. |
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| 6 | To be able to work effectively in multi-disciplinary teams within the discipline of Bioengineering; to be able to exhibit individual work. |
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| 7 | To be able to have the knowledge about the social, environmental, health, security and law implications of Bioengineering applications, to be able to have the knowledge to manage projects and business applications, and to be able to be aware of their limitations in professional life. |
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| 8 | To be able to have the social, scientific and ethical values in the stages of collection, interpretation, dissemination and application of data related to the field of Bioengineering. |
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| 9 | To be able to prepare an original thesis/term project in accordance with the criteria related to the field of Bioengineering. |
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| 10 | To be able to follow information about Bioengineering in a foreign language and to be able to participate in discussions in academic environments. |
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| 11 | To be able to improve the acquired knowledge, skills and qualifications for social and universal purposes regarding the studied area. |
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| 12 | To be able to recognize regional and global issues/problems, and to be able to develop solutions based on research and scientific evidence related to Bioengineering. |
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*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest