This draft textbook is extracted from lecture notes from a class that I have taught
(unfortunately online, but this gave me an opportunity to write more detailed notes) during …

Generative adversarial network (GAN) is a minimax game between a generator mimicking
the true model and a discriminator distinguishing the samples produced by the generator …

Adversarially robust classification seeks a classifier that is insensitive to adversarial
perturbations of test patterns. This problem is often formulated via a minimax objective …

Supervised learning is often affected by a covariate shift in which the marginal distributions
of instances (covariates $ x $) of training and testing samples $ p_\text {tr}(x) $ and $ p_\text …

Supervised classification techniques use training samples to find classification rules with
small expected 0-1 loss. Conventional methods achieve efficient learning and out-of-sample …

Top-$ k $ classification is a generalization of multiclass classification used widely in
information retrieval, image classification, and other extreme classification settings. Several …

The maximum entropy principle advocates to evaluate events' probabilities using a
distribution that maximizes entropy among those that satisfy certain expectations' …

For a sequence of classification tasks that arrive over time, it is common that tasks are
evolving in the sense that consecutive tasks often have a higher similarity. The incremental …

Supervised classification techniques use training samples to learn a classification rule with
small expected 0-1 loss (error probability). Conventional methods enable tractable learning …

Max-margin methods for binary classification such as the support vector machine (SVM)
have been extended to the structured prediction setting under the name of max-margin …