Conditional Probability: Prediction Decomposition

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Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

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

Covers probability, statistics, independence, covariance, correlation, and random variables.

Introduces key concepts in probability and statistics, covering random experiments, events, intersections, unions, and more.

Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.