Course Description
This is a graduatelevel course that teaches basics of random process theory with applications to communication theory and systems. Important topics include analysis of common random processes (e.g. Poisson process, White Noise, Wiener Process, etc.), random sequences, random processes in linear systems, Markov Chains, meansquare calculus.
The textbook used for the course is, "Probability, Statistics, and Random Processes for Engineers+, 4th Edition, by H. Stark and J. W. Woods.
The video lectures listed below provide a full outline of the course, but only portions of the lectures have been recorded so far. I try to add a few more recorded lectures each time I teach the course.
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Video Lectures

Course Introduction and Random Variable Review (8 videos, ~58 minutes)

Random Vectors: (14 videos, ~164 minutes)

Topics include: density and distribution functions, uncorrelated vectors, orthogonal vectors, transformations of random vectors, covariance matrices, eigenvalues/eigenvectors of covariance matrices, covariance diagonalization, Gaussian random vectors.


Random Sequences

Part 1: (17 videos, ~202 minutes)

Topics include: random sequence definition, mean function autocorrelation function, Bernoulli random sequence, arrival time random sequence, random walks, random sequences in discretetime linear systems


Part 2: WideSense Stationary (WSS) Random Sequences


Random Processes

Part 1: Introduction and Study of Common Random Processes (10 videos, ~87 minutes)

Part 2: Markov Random Processes and ContinuousTime Linear Systems

Part 3: Classification and WSS Random Processes

Part 4: Additional Classification


Mean Square (MS) Calculus

Part 1: Introduction, MS Derivative, and MS Integral

Part 2: Ergodicity and the KL Expansion

Part 3: Bandpass Random Processes

Example Problems

Random Vectors (5 videos, ~36 minutes)
Practice Problems
File Downloads