Probabilistic Models

Generative models that explicitly represent probability distributions, enabling calibrated uncertainty, density estimation, and principled inference under the Bayesian framework.

Notes

• Probabilistic Models — Naive Bayes, GMM, EM algorithm, Bayesian linear regression
• Probabilistic Models — Implementation — Gaussian Naive Bayes, GMM with EM, Bayesian linear regression, calibration with scikit-learn

Links

← Tree Ensembles → Neural Networks

• Bayesian Inference
• Unsupervised Learning