Covers fractional factorial designs to efficiently study interactions in experiments, focusing on aliasing, geometric interpretation, and effect selection.
Covers methods to define the design storm, empirical distribution of rainfall maxima, Gumbel distribution, and intensity-duration-frequency relationships.
Delves into the fundamental limits of gradient-based learning on neural networks, covering topics such as binomial theorem, exponential series, and moment-generating functions.
