Signal and System Estimation for Beginners

In a world driven by noisy sensor data, the ability to accurately estimate hidden states and predict system behavior is a vital skill for modern engineering. This text-based course guides you from the fundamental math of probability and linear systems to implementing powerful estimation algorithms. You will learn how to extract clean, actionable information from noisy, real-world signals. What you will learn: Understand the core principles of probability, random variables, and state-space modeling; Apply least squares and recursive least squares estimation to identify system parameters; Configure and implement Kalman filters to track dynamic states in the presence of noise; Analyze signal processing techniques for filtering and smoothing noisy data; Practice translating theoretical estimation equations into clean, readable Python code. The course begins with foundational definitions of signals, systems, and noise before walking through step-by-step mathematical derivations and practical code-based implementations. Designed for beginners in engineering, data science, or robotics, this course requires no advanced prerequisites. Start learning how to decode noisy systems today.