Data-Driven System Modeling: Foundations

Understanding how complex systems behave is crucial for prediction, optimization, and control in engineering and data science. This course introduces you to the foundational principles of system identification, a powerful methodology for constructing mathematical models directly from observed data. By engaging with the comprehensive text, you will gain the knowledge and practical skills to identify and model dynamic systems with confidence. You'll learn to transform raw data into predictive models, enabling you to make informed, data-driven decisions across various applications. What you'll learn: * Understand the core concepts and methodologies behind data-driven system identification. * Apply fundamental techniques for constructing input-output and state-space models from observational data. * Analyze different model structures, parametrization strategies, and identifiability conditions. * Implement prediction error methods for robust parameter estimation and model validation. * Explore recursive estimation techniques, including the basic principles of Kalman filtering. * Evaluate model quality and select optimal models using modern criteria for predictive accuracy. This course begins with essential definitions and theoretical foundations, then systematically guides you through various modeling techniques and methods for validating and refining your identified systems. It is designed for beginners with no prior experience in system identification, making it suitable for aspiring engineers, data scientists, and anyone interested in understanding and modeling dynamic processes using real-world data. Start your journey into understanding and modeling complex systems from data.