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This lecture covers the theory behind Maximum Likelihood Estimation (MLE), discussing properties such as consistency, asymptotic normality, and efficiency. It delves into the information matrix, asymptotic covariance matrix, and the use of MLE in various applications like binary choice models and ordered multiresponse models.