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This lecture covers state estimation in a linear Gaussian setting, focusing on the Kalman filter for multivariable control systems. It explains the assumptions, state prediction, filtering steps, and variance minimization techniques. Examples include channel estimation in communication systems and position/velocity estimation in vehicles.