Probabilistic Models

Dive into probabilistic clustering models that provide a statistical framework for understanding data distributions. Learn about Gaussian Mixture Models (GMMs), Bayesian Networks, applications of information theory in unsupervised learning, and how entropy and mutual information play a role in probabilistic clustering.

Gaussian Mixture Models (GMMs)

A comprehensive guide to Gaussian Mixture Models (GMMs), including their mathematical foundations, formulas, and practical implementation in Python.

Bayesian Networks

An in-depth exploration of Bayesian Networks, covering their structure, mathematical foundations, conditional independence, and the process of inference in these probabilistic models.

Applications of Information Theory in Unsupervised Learning

Explore the diverse applications of information theory in unsupervised learning, including clustering, anomaly detection, and dimensionality reduction.

Probabilistic Clustering Models

Explore probabilistic clustering models that leverage statistical frameworks to assign probabilities to cluster memberships. Understand models like Gaussian Mixture Models, Dirichlet Process Mixtures, and Bayesian Gaussian Mixtures, and learn how they enhance clustering robustness and flexibility.