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Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Covers conditional expectation, Fubini's theorem, and their applications in probability theory.
Explores dependence in random vectors, covering joint density, conditional independence, covariance, and moment generating functions.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Covers the fundamental concepts of probability and statistics, including interesting results, standard model, image processing, probability spaces, and statistical testing.
Covers conditional probability, probability distributions, and choosing the right distribution for specific scenarios.
