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| Course Description |
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| Course Name |
: |
Probability And Random Variables |
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| Course Code |
: |
EEE214 |
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| Course Type |
: |
Compulsory |
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| Level of Course |
: |
First Cycle |
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| Year of Study |
: |
2 |
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| Course Semester |
: |
Spring (16 Weeks) |
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| ECTS |
: |
4 |
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| Name of Lecturer(s) |
: |
Prof.Dr. SADULLAH SAKALLIOĞLU
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| Learning Outcomes of the Course |
: |
1. Can explain samples spaces, sample points
2. Can solve problems of permutation, combination
3. Can use probablity axioms
4. Can apply conditional probablity, Bayesian theorem
5. Can explain the concept of random varibale and its distribution
6. Can explain the expected value, variance and properties of a variable
7. Can use the concepts of moments and Chebyshev inequality
8. Can explain certain discrete distributions such as Bernoulli, binomial, geometric and negative binomial.
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| Mode of Delivery |
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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 provide aptitude on counting problems, to provide basic concepts such as permutation, combination and probablity, random variables |
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| Course Contents |
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Basic concepts of probability and random variables, expectation, and variance, covariance, distribution functions, bivariate marginal and conditional distributions. The Binomial and related distributions, the Poisson Process, the Exponential and Gamma distributions, the Normal distributions, the distributions of sample statistics, the Law of Large Numbers, and the Central Limit Theorem. |
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| Language of Instruction |
: |
English |
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| Work Place |
: |
Classroom |
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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 |
Smaple spaces, sample points |
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Lecture, discussion |
|
2 |
Permutation, combination |
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Lecture, discussion |
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3 |
Binomial expansion |
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Lecture, discussion |
|
4 |
Probablity of an event, axioms, rules |
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Lecture, discussion |
|
5 |
Geometri probablity, conditional probablity |
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Lecture, discussion |
|
6 |
Independent events, Bayesian theorem |
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Lecture, discussion |
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7 |
Random variable concept |
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Lecture, discussion |
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8 |
Midterm exam, problem session |
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Exam |
|
9 |
Distribution of continuos random variable |
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Lecture, discussion |
|
10 |
Expected value, variance and properties |
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Lecture, discussion |
|
11 |
Moments |
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Lecture, discussion |
|
12 |
Chebyshew inequality, problems |
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Lecture, discussion |
|
13 |
Bernoulli, Binomial, multiterm distributions |
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Lecture, discussion |
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14 |
Negative binomial, hypergeometric, Poisson distributions |
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Lecture, discussion |
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15 |
Problem session |
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Lecture, discussion |
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16/17 |
Final exam |
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Exam |
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Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
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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 |
100 |
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Homeworks/Projects/Others |
0 |
0 |
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Total |
100 |
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Rate of Semester/Year Assessments to Success |
40 |
| |
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Final Assessments
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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* |
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1 |
Has capability in those fields of mathematics and physics that form the foundations of engineering. |
4 |
|
2 |
Grasps the main knowledge in the basic topics of electrical and electronic engineering. |
2 |
|
3 |
Comprehends the functional integrity of the knowledge gathered in the fields of basic engineering and electrical-electronics engineering. |
0 |
|
4 |
Identifies problems and analyzes the identified problems based on the gathered professional knowledge. |
2 |
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5 |
Formulates and solves a given theoretical problem using the knowledge of basic engineering. |
2 |
|
6 |
Has aptitude for computer and information technologies |
0 |
|
7 |
Knows English at a level adequate to comprehend the main points of a scientific text, either general or about his profession, written in English. |
0 |
|
8 |
Has the ability to apply the knowledge of electrical-electronic engineering to profession-specific tools and devices. |
0 |
|
9 |
Has the ability to write a computer code towards a specific purpose using a familiar programming language. |
0 |
|
10 |
Has the ability to work either through a purpose oriented program or in union within a group where responsibilities are shared. |
0 |
|
11 |
Has the aptitude to identify proper sources of information, reaches them and uses them efficiently. |
2 |
|
12 |
Becomes able to communicate with other people with a proper style and uses an appropriate language. |
3 |
|
13 |
Internalizes the ethical values prescribed by his profession in particular and by the professional life in general. |
0 |
|
14 |
Has consciousness about the scientific, social, historical, economical and political facts of the society, world and age lived in. |
1 |
| * 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 |
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Class Time (Exam weeks are excluded) |
13 |
3 |
39 |
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Out of Class Study (Preliminary Work, Practice) |
13 |
3 |
39 |
| Assesment Related Works |
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Homeworks, Projects, Others |
0 |
0 |
0 |
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Mid-term Exams (Written, Oral, etc.) |
1 |
10 |
10 |
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Final Exam |
1 |
15 |
15 |
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Total Workload: | 103 |
| Total Workload / 25 (h): | 4.12 |
| ECTS Credit: | 4 |
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