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Maximum Likelihood Estimation

Finding model parameters that maximize the probability of observing the training data.

Finding model parameters that maximize the probability of observing the training data.

This concept is essential for understanding machine learning fundamentals and forms a key part of modern AI systems.

Parameter Estimation
Probability
Training

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Maximum Likelihood Estimation

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Expectation-Maximization Maximum A Posteriori

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Foundations Maximum A Posteriori Parameter estimation that incorporates prior beliefs, maximizing posterior probability rather than just likelihood. Foundations Bayesian Inference Using Bayes' theorem to update beliefs about parameters given data, incorporating uncertainty. Evaluation Log Loss Logarithmic loss measuring the accuracy of probabilistic predictions, penalizing confident wrong predictions. Training Cross-Entropy Loss A loss function for classification that measures the difference between predicted and true probability distributions. Foundations Expectation-Maximization An iterative algorithm for finding maximum likelihood estimates in models with latent variables.