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 and statistical learning theory, Bayes and naive
classifiers, nearest-neighbor rules, clustering, self-organizing maps, decision
trees, logistic regression, discriminant analysis, Perceptron, support vector
machines, kernel methods, bagging, boosting, random forests, cross-validation,
and principal component analysis.
- Professor: Ali H. Sayed
- Teacher: Stefan Vlaski