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This course aims to improve the knowledge of students on probability and random processes by providing tools for solving of the engineering problems in communications, signal processing, computer science, and other disciplines.
Basic Concepts of Probability Theory, Discrete Random Variables, One Random Variables, Pairs of Random Variables, Long-Term Averages, Bernoulli and Poisson Random Processes, Analysis and Processing of Random Signals, Markov Chains.
LO-1: Learn the basic concepts of probability theory (e.g., random experiments, axioms of probability, conditional probability, and statistical independence).
LO-2: Express discrete random variables by using CDFs, PMFs; calculate expected value of random variables. Students also learn Markov and Chebyshev inequalities.
LO-3: Identify continuous random variables, expected values, their joint PDFs, conditional probabilities, conditional expectations, correlation, and covariance.
LO-4: Understand multiple random variables including joint CDFs, PMFs, PDFs; marginal PMFs, PDFs; independent random variables, derived distributions, and conditional probability models.
LO-5: Identify iid sequences, Poisson processes, stationary processes, and cross-correlation.
LO-6: Gain knowledge in power spectral density and response of linear systems to random signals.
LO-7: Understand Markov chains and their transient behavior.