Probability Distributions: Understanding Parameters and Transformations

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Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Covers the theory and practical applications of linear regression.

Covers discrete and continuous probability distributions, including joint and conditional probabilities.

Introduces random variables, probability distributions, and expected values through practical examples.

Explores conditional density, expectations, and independence of random variables with practical examples.

Explores Gaussian mixture models and denoising noisy signals using a probabilistic approach.

Covers the concept of graphical models and joint probability distributions.

Covers the derivation of the EM algorithm for the Gaussian Mixture Model.

Explores air pollution analysis using wind data, probability distributions, and trajectory models for air quality assessment.

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.