|Semester||Semester 3 – Fall|
- Sample Space and Probability
- Probabilistic Models, Conditional Probability, Total Probability Theorem and Bayes’s Rule, Independence, Counting.
- Discrete Random Variables
- Basic Concepts, Probability Mass Function, Functions of Random Variables, Expectation, Mean and Variance, Joint PMFs of Multiple Random Variables, Conditioning, Independence.
- General Random Variables
- Continuous Random Variables, and PDFs, Cumulative Distribution Functions, Normal Random Variable, Joint PDFs of Multiple Random Variables, Conditioning, The Continuous Bayer’s Rule.
- Further Topics on Random Variables
- Derived Distributions, Covariance and Correlation, Conditional Expectation and Variance revisited, Transforms, Sum of Random Number of Independent Random Variables.
This is an introductory course in Probability Theory. It treats discrete and continuous random variables as well as basic theorems and methods and tools for problem that involve uncertainty. The student is exposed to a variety of problems mainly in the area of Electrical and Computer Engineering. Probability is a basic tool in many scientific areas as well.
Upon completion the student will be able to:
- Solve problems that involve uncertainty.
- Understand basic theory of probability
- Know basic methods and tools of solving probability problems.
- Solve problems in various scientific areas in ECE.