Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Introduces key concepts in probability and statistics, covering random experiments, events, intersections, unions, and more.
Covers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.
Covers examples of probability, including Bayes Theorem, independence, and conditional probability.
Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
Explores conditional probability, the law of total probability, Bayes' theorem, and prediction decomposition.
