School of Engineering \ Computer Engineering
Course Credit
ECTS Credit
Course Type
Instructional Language
Programs that can take the course
Computer Engineering
Artificial Intelligence Engineering
Basic concepts in probability, discrete distributions, continuous distributions, joint distributions, limit theorems, introduction to statistics, point and interval estimation, hypothesis testing.
Textbook and / or References
1. Probability and Statistics for Engineers and Scientists, 10th Edition, Walpole, R. E., Myers, R. H., Myers S. L., Ye, K.
2. Introduction to Probability, 2nd Edition, Dimitri P. Bertsekas and John N. Tsitsiklis, Athena Scientific, 2008.
3. A First Course in Probability, 8th Edition, Sheldon Ross.
4 .https: //www.probabilitycourse.com/preface.php
5. Probability and Statistics for Computer Scientists. Micheal Baron. CRC Press
6. Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis. M. Mitzenmacher and E. Upfal. Cambride University Press.
Learning the basic concepts of probability theory.
Creating probability models of frequently encountered problems in the field of computer engineering and analyzing them in this context.
Acquiring the basic competence necessary to carry out further studies.
1. Lean basic discrete random distributions.
2. Learn basic continuous random distributions.
3. Learn point and interval estimations methods.
4. Learn hypothesis testing methods.
Week 1: Fundamental Concepts of Probability
Week 2: Basic Discrete Random Variables and Their Distributions
Week 3: Basic Continuous Random Variables and Their Distributions
Week 4: Joint Distributions
Week 5: Independence, Covariance, and Correlation
Week 6: Sampling and Bootstrapping
Week 7: Limit Theorems
Week 8: Introduction to Statistics and Parameter Estimation
Week 9: Point Estimation Methods
Week 10: Interval Estimation Methods
Week 11: Hypothesis Testing
Week 12: Hypothesis Testing
| Tentative Assesment Methods |
| Activities |
Number |
Weight (%) |
| Course Attendance/Participation |
- |
- |
| Laboratory |
- |
- |
| Application |
- |
- |
| Homework |
2 |
10% |
| Project |
- |
- |
| Presentation |
- |
- |
| Field Work |
- |
- |
| Internship |
- |
- |
| Course Boards |
- |
- |
| Quiz |
- |
- |
| Midterm Exam |
1 |
40% |
| Final Exam |
1 |
60% |
|
Total |
110% |
| Tentative ECTS-Workload Table |
| Activities |
Number/Weeks |
Duration (Hours) |
Workload |
| Course Hours (first 6 weeks) |
6 |
4 |
24 |
| Course Hours (last 6 weeks) |
6 |
4 |
24 |
| Laboratory |
- |
- |
- |
| Application |
- |
- |
- |
| Homework |
2 |
15 |
30 |
| Project |
- |
- |
- |
| Presentation |
- |
- |
- |
| Field Work |
- |
- |
- |
| Internship |
- |
- |
- |
| Course Boards |
- |
- |
- |
| Preparation for Quiz |
- |
- |
- |
| Preparation for Midterm Exam |
1 |
25 |
25 |
| Final Exam |
1 |
3 |
3 |
| Preparation for Final Exam |
1 |
35 |
35 |
| Study Hours Out of Class (preliminary work, reinforcement, etc.) |
12 |
2 |
24 |
| Total Workload | | |
165 |
| Total Workload / 30 | | |
165 / 30 |
| | |
5.500000 |
| ECTS Credits of the Course | | |
6 |
|
Program Outcome
**
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2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
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Course Outcome
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| 1 |
A, C, D
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C
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| 2 |
C, D, A
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C
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| 3 |
C, D, A
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C
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| 4 |
C, D, A
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C
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