Adaptation and learning

In this course, students learn to master tools, algorithms, and core concepts related to inference from data, data analysis, and adaptation and learning theories. Emphasis is on the theoretical underpinnings and statistical limits of learning theory. In particular, the course covers topics related to optimal inference, regularization, proximal techniques, online and batch methods, stochastic learning, generalization, statistical learning 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, neural networks, and principal component analysis.