Covers the Stochastic Block Model and its application in community detection, exploring its mathematical formulation and challenges.
Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.
Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
Covers the properties of the exponential family and the estimation of parameters.
Covers methods to define the design storm, empirical distribution of rainfall maxima, Gumbel distribution, and intensity-duration-frequency relationships.
