Covers estimation, shrinkage, and penalization in statistics for data science, emphasizing the importance of balancing bias and variance in model estimation.
Explores compositions of applications and injectivity conditions in linear algebra, including restriction of applications and combinatorial proof of injections.
Explores special examples of Generalized Linear Models, covering logistic regression, count data models, separation issues, and nonparametric relationships.
