| Language of Instruction |
English |
| Course Type |
Compulsory |
| Course Level |
Masters Degree |
| Course Instructor(s) |
Meral TOSUN
mtosun@gsu.edu.tr (Email)
|
| Assistant |
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| Objective |
Probability theory is one of the most important techniques used in data processing. The aim of this course is to provide the student with some the necessary background of the probability theory for data science and related statistical applications.
|
| Content |
Conditional probability; Bayes theorem; The course includes distribution functions, binomial, geometric, hypergeometric, and Poisson distributions, uniform, exponential, normal, gamma and beta distributions; joint distributions; Chebyshev inequality; central limit theorem. Introduction to Markov chains.
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| Course Learning Outcomes |
The student who takes this course has internalized the concept of random variables and
1) Understands and applies the basic probability model consisting of probability space, relevant set algebra and probability function 2) Knows conditional probability and Bayes' rule 3) Recognizes frequently encountered distributions 4) Can calculate expected value and variance definitions with their justifications and calculate one and these invariants 4) Limit theorems 5) Introduction to discrete Markov chains and their applications.
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| Teaching and Learning Methods |
Course.
Problem solving.
Homework.
Presentation.
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| References |
Sheldon Ross, An initiation to Probability
Introduction to Probability for Data Science Stanley H. Chain
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