Maximum Likelihood Estimation (MLE)

Foundations

Maximum Likelihood Estimation (MLE)

Choosing parameters that maximise the probability of the observed data under the model.

Why interviewers ask about it

Every loss function is a negative log-likelihood: Gaussian noise gives MSE, Bernoulli gives cross-entropy. Choosing a loss is choosing a noise model.

MAP Estimation

Maximising the posterior P(θ|data) ∝ P(data|θ)P(θ) - maximum likelihood with a prior.

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