Foundations of Statistical Signal Processing

Do you want to understand how to extract meaningful information from noisy or complex data streams? Statistical Signal Processing provides the essential tools to make sense of data where randomness plays a key role. This course will equip you with the foundational knowledge and practical skills to model, analyze, and process signals using statistical methods, transforming your ability to work with data in various technical domains. What you'll learn: * Understand fundamental concepts of probability, random variables, and stochastic processes in signal analysis. * Apply estimation theory to extract meaningful parameters from noisy observations and quantify uncertainty. * Learn spectral analysis techniques for characterizing the frequency content and power distribution of signals. * Implement basic adaptive filtering algorithms to process signals in dynamic and non-stationary environments. * Explore modern applications of statistical signal processing, including basic feature extraction for machine learning. * Practice analyzing real-world signal data through structured, text-based exercises. The course begins with a thorough review of probability theory and random variables, progressing to detailed discussions on stochastic processes, estimation, and detection. You will then explore various filtering and spectral analysis methods, concluding with an introduction to their practical implications. This course is designed for absolute beginners with no prior experience in statistical signal processing, providing a step-by-step introduction to all necessary concepts. Start your journey into the world of statistical signal processing and unlock new analytical capabilities.