PERFORMANCE EVALUATION OF COMPUTER NETWORKS
- Course
- CMPE523 - PERFORMANCE EVALUATION OF COMPUTER NETWORKS
- Department
- Computer Engineering - English - Master
- Course Type
- Course
- Status
- Required
- Language
- English
- Credit
- 3
- ECTS
- 0
- T+P+L
- 3 + 0 + 0
- Course Coordinator(s)
- -
- Prerequisite
- -
- Keywords
- -
Course Description
-
PERFORMANCE EVALUATION OF COMPUTER NETWORKS
Evaluation Tools (Active Term)
No evaluation items have been defined.
Course outcomes
- 01 Apply the concepts of probability, conditional probability and conditional expectations.
- 02 Calculate probabilities, moments and other related quantities based on given distributions.
- 03 Understand and apply the laws of large numbers and central limit theorems.
- 04 Understand and apply martingale limit theory.
- 05 Understand and apply Brownian motion model.
Course Syllabus
| Week | Topic |
|---|---|
| Week 1 | Axiomatic definition of probability. Properties of probability measure. Conditional probability, stochastic independence. |
| Week 2 | Random variables, distribution functions and density functions. Expectation and moments of random variables. |
| Week 3 | Distributions of transformations of random variables. |
| Week 4 | Some probability distributions. |
| Week 5 | Multivariate random variables. Joint and conditional distributions. Stochastic independence. Expectation. Covariance and correlation. |
| Week 6 | Independence and expectation. Cauchy-Schwartz inequality |
| Week 7 | Bivariate normal distribution. Density function, moments, marginal and conditional densities |
| Week 8 | Convergence of sequences of random variables. Laws of large numbers. Central limit theorems |
| Week 9 | Kurtosis and Skewness. |
| Week 10 | - |
| Week 11 | - |
| Week 12 | - |
| Week 13 | - |
| Week 14 | - |
| Week 15 | - |
Reference Books & Course Materials
- 01 Probability, Statistics, and Random Processes for Electrical Engineering, Alberto Leon-Garcia
- 02 Probability & Statistics for Engineers & Scientists, Ronald E. Walpole
Learning Outcomes
No learning outcomes have been defined.
Program Outcomes
- Demonstrate advanced knowledge of core concepts and theoretical frameworks in Management Information Systems, and apply them to develop innovative models that reflect current advancements in the field.
- Analyze complex organizational problems and design integrated information systems solutions by applying advanced MIS knowledge and synthesizing multiple theoretical perspectives.
- Develop and manage database systems and data-driven applications that ensure effective storage, retrieval, and use of organizational data in real-world contexts.
- Utilize data analytics, business intelligence tools, and data visualization techniques to extract actionable insights and support strategic, data-driven decision-making.
- Evaluate and apply emerging technologies such as AI, IoT, and Blockchain to drive digital transformation, enhance organizational performance, and formulate original research inquiries.
- Conduct independent and collaborative research using appropriate methodologies, and communicate findings effectively through scholarly writing and presentations suited to diverse audiences.
- dentify and address ethical, legal, and societal implications in the research, development, and use of information technologies, adhering to scientific and professional standards.
- Collaborate effectively in multidisciplinary teams and demonstrate leadership in addressing complex technology-driven challenges.
- Apply project management methodologies and tools to plan, execute, and monitor IT projects within organizational settings.
- Develop and integrate information systems that align with organizational goals and strategies, supporting innovation and sustainable growth.
- Perform comprehensive systems analysis and design, including requirements gathering, modeling, prototyping, and evaluating enterprise system architectures such as ERP, CRM, and SCM.
- Assess and mitigate organizational information security risks by applying appropriate principles, technologies, and best practices in cybersecurity.
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