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Core
MLE, MAP, and Bayesian vs Frequentist
Maximum likelihood picks the parameters that make the observed data most probable; MAP adds a prior and picks the most probable parameters given the data. MAP reduces to MLE when the prior is flat, and the prior acts as regularization. Applied-AI interviews probe this to see if you understand 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
Machine Learning & Data ScienceExplain MLE vs MAP and apply Bayes' theorem to a medical-test (base-rate) problem.→Machine Learning & Data ScienceDesign an A/B test for a model change: power, sample size, significance, and the peeking problem.→Machine Learning & Data ScienceContrast L1 and L2 regularization. Why does L1 produce sparse weights?→LLM & GenAI FundamentalsWhat is catastrophic forgetting, and how do you prevent it when fine-tuning or continually training an LLM?→Machine Learning & Data ScienceExplain the Central Limit Theorem, and the difference between correlation and causation (with Simpson's paradox).→Machine Learning & Data ScienceWalk through the common probability distributions and when each applies.→
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