ML First Principles

A knowledge base explaining machine learning from the ground up — no hand-waving, just the core ideas and the math behind them.

Foundations

• Calculus
• Linear Algebra
• Probability & Distributions
• Information Theory
• Statistics & Bias-Variance
• Optimization & Gradient Descent
• Distance & Similarity

Models

• Linear Regression
• Naive Bayes
• Principal Component Analysis
• Linear Discriminant Analysis
• Decision Tree
• Random Forest
• Gradient Boosting Tree
• Convolutional Neural Network
• Hidden Markov Model