One hot encoding and Multinomial Distribution

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

Explores confidence intervals, hypothesis testing, and ROC curves in statistical analysis.

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Covers elementary distributions like Bernoulli, Binomial, Geometric, Negative Binomial, Poisson, and Multinomial.

Covers random variables, probability functions, expectations, variances, and joint distributions.

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

Explores generating functions, Laplace transform, and their role in probability distributions.

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

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

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.