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.

1. Basic probability concepts (SO B).
   
   
2. Random Variables (SO B).
   
   
3. Moments of Random Variables (SO B).
   
   
4. Special Probability Distributions (SO B).
   
   
5. Multiple Random Variables (SO B).
   
   
6. Functions of Random Variables (SO B).
   
   
7. Transform Methods (SO B).
   
   
8. Descriptive Statistics (SO B).
   
   
9. Inferential Statistics (SO B).
   
   
10. Random Processes (SO B).
    
    
11. Linear Systems with Random Inputs (SO B).
    
    
12. Special Random Processes (SO B).
    
    
13. Professional Awareness (SO I).