EE306 is a course designed for students who are curious about the applied mathematical side of electrical engineering. The main learning outcome of EE306 is that students will learn many mathematical tools that lead them to better understanding of a wide range of modern applications, including digital communications, data storage, and machine learning. These tools include, estimation, optimization, random processes, autocorrelation/spectrum, and Markov chains. A consequential learning outcome is that students will develop their analytical thinking and expand their analytical skill set. A third learning outcome is that students will visualize some of the important concepts about random signals/processes via short MATLAB projects.