Stochastic Control and Communication Fundamentals

In modern engineering and network design, systems must operate reliably despite unpredictable noise, delays, and information loss. Understanding how to control these systems under uncertainty is crucial for building robust communication networks and automated processes. This text-based course guides you through the foundational principles of stochastic control and communication, enabling you to model random processes and optimize decision-making in real-time systems. What you'll learn: Understand the fundamental concepts of probability, random variables, and stochastic processes; Model dynamical systems subject to random disturbances and noise; Apply Markov Decision Processes to formulate and solve sequential decision problems; Analyze communication channels and information transmission under uncertainty; Explore modern networked control systems where control and communication constraints intersect; Practice solving stochastic optimal control problems using dynamic programming techniques. You will begin by mastering essential terminology and basic probability models before progressing to estimation, filtering, and control strategies. Through clear written explanations and step-by-step mathematical derivations, you will learn to design robust control loops for noisy communication environments. Designed for beginners in engineering, computer science, or applied mathematics, this course requires only a basic background in calculus and introductory probability. Start reading today to master the mathematical foundations of decision-making under uncertainty.