Course Description: To provide the student with an
understanding about probability, random variables and random
processes and their applications to linear systems. Therefore, the
student will learn about the various aspects of probability such
as distribution and density functions, conditional probability and
various types of random processes such as stationary and
nonstationary, ergodic and random processes, the autocorrelation
and crosscorrelation, power spectral density, white noise and
frequency domain analysis of random signals and their evaluation
in linear systems analysis.
Repeatability:: This course may not be repeated for additional
credits.
Pre-requisites:: ECE 3512 | Minimum Grade of C- |
May not be taken concurrently.
Course Overview: In the last two decades, statistical methods
in signals and systems analysis have supplanted conventional
analyses as the dominant approach in signal processing.
In this course, we introduce students to basic concepts in
statistics, beginning with simple tools such as probability distributions,
and culiminating in advanced modeling concepts such as Markov processes.
Topics covered in this course include basic probability models,
random variables, functions of random variables, transform methods,
descriptive statistics, inferential statistics and random processes.
Applications include time series prediction, experimental design
and analysis, and machine learning.
Student Outcomes (SO):
- SO B: An ability to design and conduct laboratory
experiments as well as analyze and interpret data to improve processes.
- SO I: A recognition of the need for, and the ability to
engage in life-long learning.
Course Topics: Refer to the SOs above to understand how these
topics relate to our stated student outcomes.
- Basic probability concepts
(SO B).
- Random Variables
(SO B).
- Moments of Random Variables
(SO B).
- Special Probability Distributions
(SO B).
- Multiple Random Variables
(SO B).
- Functions of Random Variables
(SO B).
- Transform Methods
(SO B).
- Descriptive Statistics
(SO B).
- Inferential Statistics
(SO B).
- Random Processes
(SO B).
- Linear Systems with Random Inputs
(SO B).
- Special Random Processes
(SO B).
- Professional Awareness
(SO I).
Questions or comments about the material presented here can be
directed to