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ML Foundations: Information Theory and Probabilistic Modeling

Advanced quiz on likelihoods, log-likelihoods, Bayes' rule, entropy, KL divergence, and probabilistic interpretations used in ML.

10 questionsEstimated 45 min0 flagged

Session status

Draft

Question 1

1 point(s)

What is likelihood in a parametric model?

Choose the best interpretation of $p(D \mid \theta)$ as a function of $\theta$ .

How plausible the observed data is under the parameter setting

The posterior distribution after observing the data

The gradient of the loss with respect to the data

The entropy of the parameter space