Probability.

Probability theory underpins machine learning. Concepts include distributions (normal, Bernoulli, etc.), conditional probability, Bayes' theorem, expectation, and variance. Probabilistic models like Naive Bayes and Bayesian neural networks explicitly model uncertainty. Understanding probability is essential for ML model design and interpretation.