Explores the importance of causality for robust machine learning, covering ideal datasets, missing data problems, graphical models, and interference models.
Explores robust and resistant methods in linear models, emphasizing the importance of handling extreme observations and the implications of robustness in regression models.
Introduces Newton's method for solving non-linear equations iteratively, highlighting its fast convergence but also its potential failure to converge in some cases.
