Introduces key concepts in probability and statistics, such as events, Venn diagrams, and conditional probability.
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 fundamental concepts of probability and statistics, including interesting results, standard model, image processing, probability spaces, and statistical testing.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
Covers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.
Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.
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.
