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.
Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
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.
Covers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.
Explores conditional probability, the law of total probability, Bayes' theorem, and prediction decomposition.
Covers the essentials of probability, algebras, and conditional probability, including the Borel o-algebra and Poisson processes.
Introduces fundamental probability concepts, including events, complements, conditional probability, and random variables.
