ML & DS

/ML & DS_

The math underneath modern ML. Probability, statistics, information theory. Each topic is a standalone visual guide with worked examples.

Probability

• Probability Basics. The probability scale, conditional probability, combining events.
• Compound Probability. Sequential events, independence, drawing without replacement, expected value.

Statistics and distributions

• Random Variables and Distributions. Discrete vs. continuous, PMF, PDF, CDF, named distributions.
• Statistics Fundamentals. Descriptive vs. inferential, measures of center and spread, correlation.

Inference and information

• Bayes' Theorem. Prior, likelihood, posterior, sequential updating, common intuition traps.
• Information Theory. Entropy, cross-entropy, KL divergence, mutual information.