Syllabus ( CSE 553 )
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Basic information
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| Course title: |
Pattern Recognition |
| Course code: |
CSE 553 |
| Lecturer: |
Prof. Dr. Erchan APTOULA
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| ECTS credits: |
7.5 |
| GTU credits: |
3 (3+0+0) |
| Year, Semester: |
1/2, Fall and Spring |
| Level of course: |
Second Cycle (Master's) |
| Type of course: |
Area Elective
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| Language of instruction: |
English
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| Mode of delivery: |
Face to face , Lab work
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| Pre- and co-requisites: |
None |
| Professional practice: |
No |
| Purpose of the course: |
Many objects in the real world have a pattern. If these patterns expose, to develop of identification and automation systems will become easier. Threrefore, the main objective of this course is teaching students the pattern recognition methods. |
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Learning outcomes
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Upon successful completion of this course, students will be able to:
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Select the most appropriate method for applying to a given object or data.
Contribution to Program Outcomes
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Define and manipulate advanced concepts of Computer Engineering
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Use advanced knowledge of mathematics, science, and engineering
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Continuously develop their knowledge and skills in order to adapt to a rapidly developing technological environment,
Method of assessment
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Written exam
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Analyze the performance of pattern recognition methods and interpret the results.
Contribution to Program Outcomes
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Use advanced knowledge of mathematics, science, and engineering
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Follow, interpret and analyze scientific researches in the field of engineering and use the knowledge in his/her field of study
Method of assessment
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Homework assignment
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Laboratory exercise/exam
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Comprehend the effects of noise on the data that is obtained from real-life and extract patterns from these noisy data.
Contribution to Program Outcomes
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Define and manipulate advanced concepts of Computer Engineering
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Formulate and solve advanced engineering problems
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Use advanced knowledge of mathematics, science, and engineering
Method of assessment
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Written exam
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Laboratory exercise/exam
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Contents
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| Week 1: |
Introduction to pattern recognition |
| Week 2: |
Discrete events and Bayes rule |
| Week 3: |
Vectors, Expectation, Moment |
| Week 4: |
Gaussians, Introduction to Bayes decision rule |
| Week 5: |
Expected loss, Bayes risk |
| Week 6: |
Gaussian decision functions |
| Week 7: |
error bounds, ROC |
| Week 8: |
Noisy features, ML Parameter estimation
Midterm exam |
| Week 9: |
Principal Component Analysis (PCA) |
| Week 10: |
Eigen faces |
| Week 11: |
Non parametric estimation |
| Week 12: |
k-NN prediction |
| Week 13: |
Linear discriminant analysis |
| Week 14: |
The other issues |
| Week 15*: |
- |
| Week 16*: |
Final exam |
| Textbooks and materials: |
Pattern Recognition and Machine Learning, C. M. Bishop, ISBN-13: 978-0387310732, Oct. 2007. |
| Recommended readings: |
Pattern Recognition and Machine Learning, C. M. Bishop, ISBN-13: 978-0387310732, Oct. 2007. |
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* Between 15th and 16th weeks is there a free week for students to prepare for final exam.
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Assessment
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| Method of assessment |
Week number |
Weight (%) |
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| Mid-terms: |
8 |
35 |
| Other in-term studies: |
0 |
0 |
| Project: |
0 |
0 |
| Homework: |
3,6,9,12 |
25 |
| Quiz: |
0 |
0 |
| Final exam: |
16 |
40 |
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Total weight: |
(%) |
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Workload
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| Activity |
Duration (Hours per week) |
Total number of weeks |
Total hours in term |
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| Courses (Face-to-face teaching): |
3 |
14 |
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| Own studies outside class: |
4 |
14 |
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| Practice, Recitation: |
0 |
0 |
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| Homework: |
10 |
4 |
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| Term project: |
0 |
0 |
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| Term project presentation: |
0 |
0 |
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| Quiz: |
0 |
0 |
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| Own study for mid-term exam: |
20 |
1 |
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| Mid-term: |
1 |
1 |
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| Personal studies for final exam: |
21 |
1 |
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| Final exam: |
2 |
1 |
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Total workload: |
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Total ECTS credits: |
* |
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* ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
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