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AI & MLCoreAIML-CLS-009

Generative Classifiers: Gaussian Discriminant Analysis

Model each class's inputs, then classify with Bayes' rule; shared covariance gives linear boundaries, separate gives quadratic.

Difficulty
2 / 5
Importance
5 / 5
Study time
1–2 h
Kind of work
Mixed

Why it matters

The learner can switch between shared and class-specific covariances and explain why the boundary becomes linear or quadratic.

The route to this topic

Route AIML-CLS-009
Natural Numbers What Is a Model? Variables & Constants Tasks: Regression, Classification, and Beyond Features and Targets Solving Linear Equations Function Notation & Composition Probabilistic Prediction Classification Framing Linear Regression I: Prediction and Residuals Complex Numbers Scores and Logits Loss and Fitting: Squared Error Basic Ratios Unit Circle Sigmoid and Probabilities Polynomial Functions Logistic Regression Intuitive Limits Continuity Definition of the Derivative Differentiation Rules Decision Boundaries Chain Rule Antiderivatives & Indefinite Integrals Derivatives of Transcendental Functions Riemann Sums & Definite Integral Fundamental Theorem of Calculus Integration by Parts Sample Spaces & Events Random Variables & Distributions Joint Distributions The Multivariate Gaussian Generative Classifiers: Gaussian Discriminant Analysis