EECS 3300 - Probabilistic Methods Course Syllabus
Credits/Contact Hours
3 credit hours & 3 contact hours.
Textbook
Alberto Leon-Garcia, Probability, Statistics, and Random Processes for Electrical Engineering, Third Edition, 2008, Pearson,
ISBN-13:978-0-13-147122-1, or ISBN-10: 0-13-147122-8.
Course Information
Techniques for modeling and analysis of random phenomena in EECS, including communication,
control, and computer Systems. Distribution, density, and characteristic functions.
Computer generation. Function of random variables.
Prerequisite: EECS 3210 Co-Requisite: None
Required for EE majors
Specific Goals - Student Learning Objectives (SLOs)
The student will be able to:
1. Characterize probability models using probability mass function and probability
density function for discrete and continuous random variables.
2. Describe conditional and independent events and conditional random variables.
3. Evaluate the mean and variance of different distributions
4. Calculate the cumulative distribution functions for both discrete and continuous
random variables.
5. Characterize functions of random variables
6. Characterize jointly multiple discrete and continuous random variables
7. Use computer software to generate probability distribution functions
Topics
- Probability models
- Concepts of probability theory
- Probability distribution
- Density functions
- One/multiple random variables
- Sum of random variables