Covers conditional expectation, Fubini's theorem, and their applications in probability theory.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Introduces the basics of conditional expectation, covering definitions, properties, and examples in the context of random variables.
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, covering random experiments, events, intersections, unions, and more.
Introduces key concepts in probability and statistics, such as events, Venn diagrams, and conditional probability.
Covers the description of states in quantum mechanics and their relation to probability, expectation values, and the Schrödinger equation.
Explores tail bounds, information bounds, and maximal leakage in the context of generalization error.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Covers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.
