PROBABILITY AND STOCHASTIC PROCESS
- Course
- CMPE613 - PROBABILITY AND STOCHASTIC PROCESS
- Department
- Computer Engineering - English - PhD
- Course Type
- Course
- Status
- Required
- Language
- English
- Credit
- 3
- ECTS
- 0
- T+P+L
- 3 + 0 + 0
- Course Coordinator(s)
- -
- Prerequisite
- -
- Keywords
- -
Course Description
-
PROBABILITY AND STOCHASTIC PROCESS
Evaluation Tools (Active Term)
No evaluation items have been defined.
Course outcomes
No course outcomes have been defined yet.
Course Syllabus
| Week | Topic |
|---|---|
| Week 1 | Introduction to Probability |
| Week 2 | Axioms and Basic Theorems of Probability |
| Week 3 | Random Variables |
| Week 4 | Expectation, Variance, and Moments |
| Week 5 | Transformation of Random Variables |
| Week 6 | Random Sequences and Convergence |
| Week 7 | Statistics of Stochastic Processes |
| Week 8 | Mid-term Exam |
| Week 9 | Stationarity and Properties |
| Week 10 | Linear Systems with Random Inputs |
| Week 11 | Power Spectrum and Estimation |
| Week 12 | ARMA Processes |
| Week 13 | Markov Chains |
| Week 14 | Queueing Systems & Applications |
| Week 15 | Final Exam |
Reference Books & Course Materials
- 01 Alberto Leon-Garcia, Probability, Statistics, and Random Processes for Electrical Engineering, 3rd Edition, Pearson.
- 02 Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying Ye, Probability & Statistics for Engineers & Scientists, 9th Edition, Pearson.
Learning Outcomes
No learning outcomes have been defined.
Program Outcomes
- Demonstrate mastery of advanced research methodologies (quantitative, qualitative, and mixed methods) by critically analyzing literature, identifying research gaps, and designing original studies that contribute to MIS theory and practice.
- Conduct and defend an original doctoral dissertation that reflects independent scholarly inquiry, academic rigor, and a significant contribution to the advancement of knowledge in MIS.
- Exhibit readiness for thesis monitoring and defense by articulating the philosophical foundations of research paradigms, positioning one's research within these frameworks, and responding to scholarly critique.
- Apply ethical principles, academic integrity, and responsible conduct of research in all phases of the research process, including data collection, analysis, reporting, and publication.
- Identify and address the social, legal, and ethical implications of information systems research and its applications within organizational and societal contexts.
- Employ advanced data science techniques, including statistical modeling, machine learning, and AI-based analytics, to examine complex datasets and extract meaningful insights in MIS research.
- Recognize and evaluate emerging technologies such as artificial intelligence, big data, blockchain, and the Internet of Things, assessing their transformative impact on organizational processes and digital ecosystems.
- Collaborate and lead in interdisciplinary research environments, establishing productive scientific partnerships and managing research projects that integrate diverse academic perspectives.
- Publish high-quality research in peer-reviewed journals, present findings at international conferences, and actively engage in academic service such as journal reviewing, conference organizing, and committee participation.
- Identify challenges and propose innovative, research-based solutions at the intersection of information systems, technology, and organizational strategy.
- Deliver advanced-level MIS courses, supervise graduate research, and nurture academic development through effective mentorship and scholarly teaching.
- Develop advanced information systems and decision support systems that align IT capabilities with organizational strategies using systems thinking and innovative design methodologies.
Po-Lo Matrix
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