Learning objectives
- • Apply Bayes theorem to detection, diagnosis, and ranking examples
- • Know when binomial, Poisson, and Gaussian assumptions are reasonable
- • Explain calibration and uncertainty in plain language
Math & Stats
Probability Toolkit
Refresh conditional probability, Bayes intuition, and core distributions used in ML reasoning.
Recommended on day 180 minutesBeginner
Interview prompts
- • Why can a model with strong recall still have weak positive predictive value?
- • How does class imbalance change your probabilistic interpretation?
Prerequisites
No strict prerequisites. This topic can be used as an entry point.