Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Introduces key concepts in probability and statistics, such as events, Venn diagrams, and conditional probability.
Introduces key concepts in probability and statistics, covering random experiments, events, intersections, unions, and more.
Explores conditional probability, the law of total probability, Bayes' theorem, and prediction decomposition.
Covers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.
