Data Science Foundations

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Related lectures (29)

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Elements of Statistics: Probability and Random Variables

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Mean-Square-Error Inference

Covers the concept of mean-square-error inference and optimal estimators for inference problems using different design criteria.

Information Theory: Channel Capacity and Convex Functions

Explores channel capacity and convex functions in information theory, emphasizing the importance of convexity.

Modes of Convergence of Random Variables

Covers the modes of convergence of random variables and the Central Limit Theorem, discussing implications and approximations.

Central Limit Theorem: Properties and Applications

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

Quantifying Statistical Dependence: Covariance and Correlation

Explores covariance, correlation, and mutual information in quantifying statistical dependence between random variables.

Convergence of Random Variables

Explores independent and identically distributed random variables, convergence, and distribution properties.

Law of Large Numbers, Statistics

Covers the Law of Large Numbers in Statistics and methods for deriving estimators and maximum likelihood.

Probability and Statistics: Fundamentals

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