Explores dependence in random vectors, covering joint density, conditional independence, covariance, and moment generating functions.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Explores independence and covariance between random variables, discussing their implications and calculation methods.
Introduces the basics of conditional expectation, covering definitions, properties, and examples in the context of random variables.
Covers Gaussian random variables, affine transformations, and linear systems driven by Gaussian noise in multivariable control.
