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Core
MLE, MAP, and Bayesian vs Frequentist
Maximum likelihood chooses the parameters that make the observed data most probable; MAP adds a prior and chooses the most probable parameters given the data. MAP reduces to MLE when the prior is flat, and the prior serves as regularization. AI, ML, and GenAI engineer interviews probe this to check whether you know where priors enter your models, why L2 regularization is a Gaussian prior in disguise, and the practical split between point estimates and full posteriors.
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RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
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