All of Probability: LLN, CLT, Chernoff and PAC bound

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Related lectures (28)

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Probability and Statistics: Fundamental Theorems

Explores fundamental theorems in probability and statistics, joint probability laws, and marginal distributions.

Law of Large Numbers: Strong Convergence

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

Central Limit Theorem: Properties and Applications

Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.

Variance and Covariance: Properties and Examples

Explores variance, covariance, and practical applications in statistics and probability.

Elements of Statistics: Probability, Distributions, and Estimation

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Continuous Random Variables

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

Probability and Statistics: Fundamentals

Covers the fundamental concepts of probability and statistics, including interesting results, standard model, image processing, probability spaces, and statistical testing.

Statistical Analysis: Boxplot and Normal Distribution

Introduces statistical analysis concepts like boxplot and normal distribution using real data examples.

Elements of Statistics

Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.

Generalization Error

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