Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
Covers the multinomial distribution, joint density, marginal distribution, and conditional distribution.
Introduces the Naive Bayes classifier, covering independence assumptions, conditional probabilities, and applications in document classification and medical diagnosis.
Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.
Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.
Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
