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.

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

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