Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Covers the fundamental concepts of probability and statistics, including interesting results, standard model, image processing, probability spaces, and statistical testing.
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 dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.
Introduces fundamental probability concepts, including events, complements, conditional probability, and random variables.
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.
