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.
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 fundamental concepts in probability and statistics, including the law of total probability, Bayes' theorem, and independence of events.
Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
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.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
Explores the Law of Total Probability and its applications in real-world scenarios, introducing key concepts in probability theory.
