Feedback in Engineering and Living Systems Benefits of Feedback Control Challenges of Feedback Control Feedback Turbulence Control is a Grand Challenge Problem Nature Teaches Us the Control Design Machine Learning Control (MLC Methods of Machine Learning System Identification as Machine Learning Genetic Algorithms Genetic Programming Additional Machine Learning Methods . . MLC with Genetic Programming Control Problem Parameterization of the Control Law Genetic Programming as a Search Algorithm Initializing a Generation Evaluating a Generation Selecting Individuals for Genetic Operations Selecting Genetic Operations Advancing Generations and Stopping Criteria Fitting a Function Through Data Points MLC Applied to Control a Dynamical System Suggested Reading Interview with Professor Marc Schoenauer Methods of Linear Control Theory Linear Systems Full-State Feedback Sensor-Based State Estimation Sensor-Based Feedback System Identification and Model Reduction System Identification Eigensystem Realization Algorithm (ERA) Observer Kalman Filter Identification (OKID) Suggested Reading Benchmarking MLC Against Linear Control Comparison of MLC with LQR on a Linear Oscillator Comparison of MLC with Kalman Filter on a Noisy Linear Oscillator Comparison of MLC with LQG for Sensor-Based Feedback Modifications for Small Nonlinearity Interview with Professor Shervin Bagheri Taming Nonlinear Dynamics with MLC Generalized Mean-Field System Machine Learning Control Formulation of the Control Problem Parameters MLC Results Derivation Outline for the Generalized Mean-Field Model Alternative Control Approaches Open-Loop Forcing Closed-Loop Forcing Short-Term Forcing Suggested Reading Interview with Professor Mark N. Glauser Taming Real World Flow Control Experiments with MLC Separation Control Over a Backward-Facing Step Flow Over a Backward-Facing Step Experimental Setup at PMMH Separation Control of Turbulent Boundary Layers Separating Boundary Layers Experimental Setups at LML and PRISME Control of Mixing Layer Growth Experimental Setup of the TUCOROM Wind Tunnel Alternative Model-Based Control Approaches Implementation of MLC in Experiments Real-Time Control Loop-from Sensors to Actuators MLC Implementation in the PMMH Flow Over a Backward-Facing Step MLC Implementation in the LML and PRISME MLC Implementation in the TUCOROM Experiment Suggested Reading Interview with Professor David Williams MLC Tactics and Strategy The Ideal Flow Control Experiment Desiderata of the Control Problem-From the Definition to Hardware Choices Cost Function Actuators Sensors Search Space for Control Laws Time Scales of MLC Controller Response Time of the Plant Learning Time for MLC MLC Parameters and Convergence Convergence Process and Its Diagnostics Parameters Pre-evaluation The Imperfect Experiment Noise Drift Monitoring Future Developments Methodological Advances of MLC System-Reduction Techniques for MLC-Coping with High-Dimensional Input and Output Future Applications of MLC Interview with Professor Belinda Batten Matlab® Code: OpenMLC.