Derivation of EM for the GMM

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Covers logistic regression and maximum likelihood estimation in depth.

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Covers the Stochastic Block Model and its application in community detection, exploring its mathematical formulation and challenges.

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Covers the Likelihood Ratio Test and hypothesis testing methods using Maximum Likelihood Estimators.

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