Adaptation and learning

The course covers the fundamentals of inference and learning from streaming and batch data. Students also learn about the foundations of online and batch machine learning techniques in a unified treatment. In particular, the course covers topics related to optimal inference, regularization, proximal techniques, stochastic learning, generalization theory, Bayes and naive classifiers, nearest-neighbor rules, clustering, decision trees, logistic regression, discriminant analysis, Perceptron, support vector machines, kernel methods, bagging, boosting, random forests, cross-validation, principal component analysis, and neural networks.