At this stage, students have already learned about probability and random variables in EE230. Moreover, they have learned about deterministic signals and systems in EE301. In EE306, EE230 and EE301 meet each other, and students learn about random/stochastic signals and processes, ones they encounter in real-life applications. Students will learn a variety of methods and tools that are adopted in a wide range of modern applications, including artificial intelligence, digital communications, and data storage. In particular,

• After probability and linear algebra reviews, students will learn about vectors and sums of random variables. They will learn parameter and random signal estimation. They will also learn basics of optimization as well as the gradient descent algorithm, which is used in machine learning.
• Students will learn the concept of random processes. They will learn autocorrelation and autocovariance. They will learn about stationarity and Gaussian processes, including white noise. They will also learn how to find and use the power spectral density of random processes.
• As examples of famous discrete stochastic processes, students will learn about Markov chains (transition probabilities, convergence and steady-state probabilities, ergodicity) and Poisson processes (counting processes, waiting time distribution).