Course Description


This is a graduate-level 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, mean-square 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 discrete-time linear systems

    • Part 2: Wide-Sense Stationary (WSS) Random Sequences

  • Random Processes

  • 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

Practice Problems


File Downloads

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© 2020 by Adam Panagos