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| Course Description |
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| Course Name |
: |
Stochastic Processes for Computer Engineers |
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| Course Code |
: |
CENG-562 |
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| Course Type |
: |
Optional |
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| Level of Course |
: |
Second Cycle |
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| Year of Study |
: |
1 |
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| Course Semester |
: |
Spring (16 Weeks) |
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| ECTS |
: |
6 |
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| Name of Lecturer(s) |
: |
Assoc.Prof.Dr. ZEKERİYA TÜFEKÇİ
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| Learning Outcomes of the Course |
: |
Learns the basic probability topics
Learns random processes
Learns the fundamentals of dedection, and Estimation
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| Mode of Delivery |
: |
Face-to-Face |
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| Prerequisites and Co-Prerequisites |
: |
None |
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| Recommended Optional Programme Components |
: |
None |
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| Aim(s) of Course |
: |
To gain basic knowledge about the random processes |
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| Course Contents |
: |
Random processes, analysis and processing of random signals, Markov chains, queueing theory. |
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| Language of Instruction |
: |
English |
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| Work Place |
: |
Classroom 2 |
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Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
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1 |
Probability |
Reading |
Lecture |
|
2 |
Probability |
Reading, homework |
Lecture |
|
3 |
Probability |
Reading |
Lecture |
|
4 |
Random Processes |
Reading, homework |
Lecture |
|
5 |
Random Processes |
Reading |
Lecture |
|
6 |
Random Processes |
Reading, homework |
Lecture |
|
7 |
Random Processes |
Reading |
Lecture |
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8 |
Midterm exam |
Reading |
Written exam |
|
9 |
Random Processes |
Reading, homework |
Lecture |
|
10 |
Random Processes |
Reading |
Lecture |
|
11 |
Random Processes |
Reading, homework |
Lecture |
|
12 |
Detection |
Reading |
Lecture |
|
13 |
Detection |
Reading, homework |
Lecture |
|
14 |
Estimation |
Reading |
Lecture |
|
15 |
Estimation |
Reading, homework |
Lecture |
|
16/17 |
Final exam |
Reading |
Written exam |
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|
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Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
 Random Signals Detection, Estimation and Data Analysis. K. Sam Shanmugan and A. M. Breipohl
Probability and Random Processes with application to Signal Processing. Henry Stark John W. Woods
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| |
| Required Course Material(s) | |
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Assessment Methods and Assessment Criteria |
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Semester/Year Assessments |
Number |
Contribution Percentage |
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Mid-term Exams (Written, Oral, etc.) |
1 |
50 |
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Homeworks/Projects/Others |
7 |
50 |
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Total |
100 |
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Rate of Semester/Year Assessments to Success |
40 |
| |
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Final Assessments
|
100 |
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Rate of Final Assessments to Success
|
60 |
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Total |
100 |
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
|
1 |
Reaches wide and deep knowledge through scientific research in the field of computer engineering, evaluates, implements, and comments. |
4 |
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2 |
Describes and uses information hidden in limited or missing data in the field of computer engineering by using scientific methods and integrates it with information from various disciplines. |
4 |
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3 |
Follows new and emerging applications of computer engineering profession, if necessary, examines and learns them |
2 |
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4 |
Develops methods and applies innovative approaches in order to formulate and solve problems in computer engineering. |
4 |
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5 |
Proposes new and/or original ideas and methods in the field of computer engineering in developing innovative solutions for designing systems, components or processes. |
3 |
|
6 |
Designs and implements analytical modeling and experimental research and solves the complex situations encountered in this process in the field of Computer Engineering |
3 |
|
7 |
works in multi disciplinary teams and takes a leading role and responsibility. |
2 |
|
8 |
Learns at least one foreign language at the European Language Portfolio B2 level to communicate orally and written |
2 |
|
9 |
Presents his/her research findings systematically and clearly in oral and written forms in national and international meetings. |
1 |
|
10 |
Describes social and environmental implications of engineering practice. |
1 |
|
11 |
Considers social, scientific and ethical values in collection, interpretation and announcement of data. |
3 |
|
12 |
Acquires a comprehensive knowledge about methods and tools of computer engineering and their limitations. |
4 |
| * Contribution levels are between 0 (not) and 5 (maximum). |
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| Student Workload - ECTS |
| Works | Number | Time (Hour) | Total Workload (Hour) |
| Course Related Works |
|
Class Time (Exam weeks are excluded) |
14 |
3 |
42 |
|
Out of Class Study (Preliminary Work, Practice) |
14 |
4 |
56 |
| Assesment Related Works |
|
Homeworks, Projects, Others |
7 |
4 |
28 |
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Mid-term Exams (Written, Oral, etc.) |
1 |
10 |
10 |
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Final Exam |
1 |
10 |
10 |
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Total Workload: | 146 |
| Total Workload / 25 (h): | 5.84 |
| ECTS Credit: | 6 |
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