University :Mansoura University |
Faculty :Faculty of Computers and Information |
Department :Computer Science |
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1- Course data :- |
| Code: | احص 232 | Course title: | probability & statistical Dist | Year/Level: | ثانية القسم العام | Program Title: | | Specialization: | | Teaching Hours: | Theoretical: | 3 | Tutorial: | 3 | Practical: | |
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2- Course aims :- |
| - To provide a solid introduction to probability theory and statistics. With this background the students should be able to specify and solve simple probabilistic problems. They should gain also some practice in working with basic statistical methods.
- Uncertainty and randomness, random experiments, probability spaces and events, sets and fields, probability distributions, probability approaches and measures, joint and conditional probability, dependent and independence.
- Probability by counting methods, permutations, combinations, Bernoulli trials, binomial probability law and applications.
- Single and pairs of random variables, discrete and continuous random variables, functions of random variables, joint and conditional distribution functions, important distribution functions and their applications in electrical engineering.
- Expected value and moments of random variables, mean and variance, correlation and covariance of pairs of random variables.
- Sums of random variables, sample mean and sample variance, the law of large numbers, central limit theorem, sampling distribution and confidence intervals.
- Applying methods of probability and statistics in computer science fields such as Natural Language Processing, Digital Signal Processing, Simulation, Machine Learning, Uncertainty, and Data and Text Mining.
- Developing skill in using computer methods for solving problems in probability and statistics.
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3- Course Learning Outcomes :- |
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4- Course contents :- |
| No | Topics | Week |
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1 | Continuous Random Variables | | 2 | Jointly Distributed Random Variables | | 3 | Properties of Expectation | | 4 | Limit Theorems | | 5 | Additional Topics in Probability | | 6 | Simulation | | 7 | Axioms of Probability | | 8 | Conditional Probability and Independence | | 9 | Random Variables | | 10 | Discrete Distributions | |
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5- Teaching and learning methods :- |
| S | Method |
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| Computer + Data Show + power point slides. | | Blackboard. | | Free Discussion. | | Different Software | | The presentation is problem-oriented and motivated by practical examples. | | Assignments are given which should be worked out independently and presented in the lecture. |
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6- Teaching and learning methods of disables :- |
| No data found. |
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7- Student assessment :- |
| A. Timing |
| No | Method | Week |
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1 | Mid-Term exam and Reports | 14 | 2 | Oral and Practical | 15 |
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| B. Degree |
| No | Method | Degree |
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1 | Mid_term examination | 5 | 2 | Final_term examination | 75 | 3 | Oral examination | 5 | 4 | Practical examination | 5 | 5 | Semester work | 5 | 6 | Other types of asessment | 5 | Total | 100% |
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8- List of books and references |
| S | Item | Type |
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1 | Ahmed E. Naghamish, "Probability Theory and Statistical Distributions for Computer Science", 2006. | | 2 | John Willey & Sons. Probability and Statistics with Reliability, Queuing, and Computer Science Applications, 2nd Ed, 2001. | | 3 | Sheldon Ross. A First Course in Probability, 5th Ed, 1998. | | 4 | Ronald E. Walpole, Raymond H. Myers and Sharon L. Myers, Probability and Statistics for Engineers and Scientists, 6th Ed, 1998. | | 5 | Different Web Sites | | 6 | Different Periodicals. | |
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9- Matrix of knowledge and skills of the course |
| S | Content | Study week |
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| Continuous Random Variables | | | Jointly Distributed Random Variables | | | Properties of Expectation | | | Limit Theorems | | | Additional Topics in Probability | | | Simulation | | | Axioms of Probability | | | Conditional Probability and Independence | | | Random Variables | | | Discrete Distributions | |
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Course Coordinator(s): - |
| - El Sayed Fouad Hassan Radwan
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Head of department: - |
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