Lectures related to Derivation of EM for the GMM

Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.

Introduces the Boltzmann Machine, covering expectation consistency, data clustering, and probability distribution functions.

Data Classification: Gaussian Mixture Models

Explores Gaussian Mixture Models for data classification, focusing on denoising signals and estimating original data using likelihood and posteriori approaches.

Statistical Estimation Methods

Covers statistical estimation methods, including maximum likelihood and Bayesian estimation.

Binary Covariate Impact: 2x2 Contingency Tables

Explores the impact of a binary covariate on binary responses using 2x2 tables.

Jacamar Data Analysis

Covers jacamar data analysis, smoking data models, and challenges with log-linear models in visual impairment data.

K-means and Gaussian Mixture Model

Introduces K-means clustering, the Gaussian mixture model, Jensen's inequality, and the EM algorithm.

Latent Variable Models

Explores latent variable models, EM algorithm, and Jensen's inequality in statistical modeling.