Explore an extensive lecture series on data-driven control and machine learning applications in control theory. Delve into topics such as linear system identification, balanced model reduction, eigensystem realization algorithm, and dynamic mode decomposition with control. Learn about advanced techniques like sparse identification of nonlinear dynamics, model predictive control, and various machine learning control methods including genetic algorithms and reinforcement learning. Gain practical insights through Matlab examples and Simulink demonstrations. Discover how to apply these concepts to real-world problems, such as tuning PID controllers and developing nonlinear aeroelastic models for morphing wings. Based on Chapters 9 & 10 from "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz, this comprehensive series provides a deep understanding of cutting-edge optimization techniques in control theory.