Statistics and Probability

Delve into the fundamental statistical and probabilistic concepts that form the backbone of unsupervised clustering techniques. Topics include Bayesian Networks, probability density functions, distribution estimation methods, the Expectation-Maximization (EM) Algorithm, entropy, mutual information, and information gain in clustering.

Understanding Probability Density Functions in Clustering

A detailed examination of Probability Density Functions (PDFs) in clustering, exploring their role in various clustering algorithms, including Gaussian Mixture Models.

Entropy and Mutual Information

A comprehensive guide to understanding entropy and mutual information, including their mathematical foundations, significance in unsupervised learning, and real-world applications.

Information Gain in Clustering

An in-depth exploration of information gain in clustering, discussing its role, calculation, and significance in unsupervised learning algorithms.

Estimating Distributions with Unsupervised Methods

An in-depth exploration of how unsupervised learning methods are used to estimate probability distributions, including techniques like clustering, density estimation, and generative models.

Expectation-Maximization (EM) Algorithm

A comprehensive exploration of the Expectation-Maximization (EM) Algorithm, covering its theory, mathematical foundations, and applications in clustering, particularly in Gaussian Mixture Models (GMMs).