Gaussian Mixture Models: Data Classification

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Covers variational inference and neural networks for classification tasks.

Explores Matlab programming with scripts and functions, vectorization, and 2D graphics.

Covers essential MATLAB functions, variables, loops, and debugging tools.

Covers the basics of LabVIEW, including its importance, history, functions, and tools available.

Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.

Covers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.

Explores the Monte Carlo method for thermal radiation and radiative energy exchange.

Covers specular view factors, radiative exchange, energy transfer, and numerical integration methods in thermal radiation.

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.

Covers conditions, loops, functions, and graphics in R programming with practical examples.