This course explores random phenomena both qualitatively and mathematically, and teach how to manipulate those descriptions to solve engineering problems. The course builds on introductory courses on probability theory to understand random behavior of systems, sequences and processes, commonly found in many engineering disciplines. We will use Bayes’ rule as the starting point and study properties of distributions of random variables and its functions, Expectation and moments. This is followed by Random vectors and sequences and finally random processes. Emphasis will be given on Limit theorems – weak and strong law of large numbers, central limit theorem. Convergence of sequences of random variables – in probability, in distribution and almost surely. Introduction to the Poisson process, Markov chains and other selected topics and applications will be covered as time permits. The knowledge acquired in this class is the foundation for courses like Statistical Signal Processing, Detection and Estimation Theory, Advanced Digital Communications, Information Theory and Machine Learning.

Prerequisites: AMAT 370 or Graduate Student Standing

Time and Location: T/R 2.45-4.05pm @ BB B003
Instructor: Aveek Dutta ([email protected])
Office hours: F 10.00am - 12.00pm @ LI 90B (or by appointment)
Blackboard: IECE 571 / CSCI 660 - Probability and Random Processes

Textbook: Probability, Statistics and Random Processes For Engineers - Henry Stark and John W. Woods

Other Reference Books:
Probability and Random Processes - Geoffrey Grimet and David Stirzaker
Probability and Random Processes for Electrical and Computer Engineers - John Grubner

1. Homework - 25%
2. Mid-term - 35%
3. Final Exam - 40%