Gaussian Mixture Model: EM final form

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Maximum Likelihood Decision

Explains decision by maximum likelihood and the use of logarithms.

Maximum Likelihood Theory & Applications

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Unsupervised Machine Learning: Clustering Basics

Introduces unsupervised machine learning clustering techniques like K-means, Gaussian Mixture Models, and DBSCAN, explaining their algorithms and applications.

Quantifying Statistical Dependence

Delves into quantifying statistical dependence through covariance, correlation, and mutual information.

Topic Models: Understanding Latent Structures

Explores topic models, Gaussian mixture models, Latent Dirichlet Allocation, and variational inference in understanding latent structures within data.

Topic Models

Introduces topic models, covering clustering, GMM, LDA, Dirichlet distribution, and variational inference.

Probability and Statistics

Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.

Precipitation and Hydrologic Design

Covers methods to define the design storm, empirical distribution of rainfall maxima, Gumbel distribution, and intensity-duration-frequency relationships.

Clustering: k-means

Explains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.