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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.
Introduces the Naive Bayes classifier, covering independence assumptions, conditional probabilities, and applications in document classification and medical diagnosis.
Covers Gaussian random variables, affine transformations, and linear systems driven by Gaussian noise in multivariable control.
Covers the basics of discrete probability, including notations, axioms, pmf, examples, expectation, variance, and indicator variables.
