ML & DS

/ML & DS_

The mathematical foundations behind modern ML systems — probability, statistics, and information theory. Each topic is a standalone visual guide with worked examples.

Probability

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

Statistics & Distributions

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

Inference & Information

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