This course enhances students' understanding of probability and random variables, equipping them with essential tools to address engineering challenges in various fields, including communications, signal processing, and other relevant disciplines. Through theoretical foundations and practical applications, the course provides students with the expertise to analyze and solve complex problems in real-world scenarios.
MATLAB with Communications Toolbox (https://www.mathworks.com/products/communications.html)
Upon successful completion of this course, students will be able to:
(1) Explain the basic probability theory concepts, including random experiments, axioms, conditional probability, and independence,
(2) Express probability mass function (PMF), cumulative distribution function (CDF), expected value, and variance for discrete random variables,
(3) Use probability density function (PDF) and CDF to investigate continuous random variables,
(4) Analyze joint probability distributions of multiple random variables, marginal distributions, correlation, and covariance,
(5) Evaluate the probability distribution of a function of random variables,
(6) Formulate conditional probability distribution functions for discrete and continuous random variables.
(1) Leon-Garcia, A. (2008). Probability, Statistics, and Random Processes For Electrical Engineering. 3rd Ed., Pearson.
(2) Bertsekas, D. P., & Tsitsiklis, J. N. (2002). Introduction to Probability. 2nd Ed., Athena Scientific.
Yates, R. D., & Goodman, D. J. (2014). Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers. 3rd Ed. Intl. Student Version, Wiley.
Test/Exam (85%), Active Learning Exercises (15%)
| Workload | Hrs |
|---|---|
| Lectures | 42 |
| Course Readings | 42 |
| Exams/Quizzes | 42 |
| Active Learning Exercises | 24 |