Maximum Likelihood Decision

Related lectures (30)

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Covers supervised learning through likelihood maximization to find optimal parameters.

Covers eigenvectors, principal components, likelihood variables, EM algorithm, Jensen's inequality, and maximizing lower bounds.

Covers the Bayes discriminant rule for allocating individuals to populations based on measurements and prior probabilities.

Explores independence and conditional probability in probability and statistics, with examples illustrating the concepts and practical applications.

Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.

Introduces K-means clustering, the Gaussian mixture model, Jensen's inequality, and the EM algorithm.

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

Explores model selection using AIC and BIC criteria, addressing different questions and the importance of sparsity in selecting the best model.