Covers Gaussian random variables, affine transformations, and linear systems driven by Gaussian noise in multivariable control.
Explores Gaussian vectors, moment generating functions, independence, density functions, affine transformations, and quadratic forms.
Explores isometries in Euclidean spaces, including translations, rotations, and linear symmetries, with a focus on matrices.
Explores data augmentation as a key regularization method in deep learning, covering techniques like translations, rotations, and artistic style transfer.
