Poisson Paradigm: Qualitative / Quantitative

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Related lectures (26)

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Generalization Error

Explores tail bounds, information bounds, and maximal leakage in the context of generalization error.

Random Variables and Covariance

Covers random variables, variances, and covariance, as well as the probability in random graphs.

Probability Theory: Lecture 2

Explores toy models, sigma-algebras, T-valued random variables, measures, and independence in probability theory.

Probabilistic Methods in Combinatorics

Covers probabilistic methods in combinatorics, monochromatic edges, 2-colorable graphs, and good 2-colorings.

Conditional Expectation

Covers conditional expectation, Fubini's theorem, and their applications in probability theory.

Probability Theory: Lecture 3

Explores random variables, sigma algebras, independence, and shift-invariant measures, emphasizing cylinder sets and algebras.

Central Limit Theorem

Explores the Central Limit Theorem, convergence in law, characteristic functions, and moment problems in probability theory.

Law of Large Numbers: Strong Convergence

Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.

Probability and Statistics: Independence and Conditional Probability

Explores independence and conditional probability in probability and statistics, with examples illustrating the concepts and practical applications.

Probability and Statistics

Introduces key concepts in probability and statistics, illustrating their application through various examples and emphasizing the importance of mathematical language in understanding the universe.