School of Engineering \ Computer Engineering
Course Credit
ECTS Credit
Course Type
Instructional Language
Programs that can take the course
Computer Engineering
Artificial Intelligence Engineering
This course is an introductory level course that teaches probability theory. The aim of the course is to teach students the basic concepts of probability theory and to show the application of these concepts in solving various problems encountered in the field of computer engineering. Within the scope of the course, topics including probability spaces, random variables, distributions, moments, independence, conditional probability and limit theorems are examined in detail.
Textbook and / or References
1. Introduction to Probability, 2nd Edition, Dimitri P. Bertsekas and John N. Tsitsiklis, Athena Scientific, 2008.
2. A First Course in Probability, 8th Edition, Sheldon Ross.
3. https://www. probabilitycourse. com/preface. php
4. Probability and Statistics for Computer Scientists. Micheal Baron. CRC Press
5. Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis. M. Mitzenmacher and E. Upfal. Cambride University Press.
6. Probability and Statistics for Engineers and Scientists, 10th Edition, Walpole, R. E. , Myers, R. H. , Myers S. L. , Ye, K.
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. Fundamentals of counting and establishing the relationship of combinatoric analysis with probability
2. Learning probability axioms, conditional probability and independence concepts
3. Learning discrete random variables and their types, expected value, and moment calculations
4. Extension of the same concepts for continuous random variables and limit theorems
5. Learning sampling, estimation and shortcutting methods at a basic level within the scope of introduction to statistics.
Week 1: Fundamentals of counting and probability
Week 2: Probability axioms
Week 3: Conditional probability and independence
Week 4: Random variable, Expected values and moments
Week 5: Basic discrete random variable types
Week 6: Continuous random variables and moments
Week 7: Combined distribution
Week 8: Independence, covariance, and correlation
Week 9: Sampling and bootstrapping
Week 10: Limit theorems
Week 11: Introduction to forecasting and estimation methods
Week 12: Introduction to forecasting and estimation methods
Tentative Assesment Methods
• Midterm 40 %
• Final 60 %
• Bonus homework or quiz: 10 % (If the total of midterm + Final is 50 %, then it will be taken into account)
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C
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C
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C
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A
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C
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