Machine learning algorithms: nearest-neighbor, regression, classification, perceptron, kernel methods, support vector machine (SVM), logistic regression, ensemble methods, boosting, graphical models, hidden Markov models (HMM), non-parametrics, online learning, active learning, clustering, feature selection, parameter tuning, and cross-validation. Students cannot receive credit for both CSCI 4364 taken while an undergraduate and CSCI 6364 taken while a graduate student. Students in the combined BS/MS program cannot receive credit for both CSCI 4364 and CSCI 6364.
As a result of taking this course, students should be able to apply machine learning techniques to practical problems. They should also develop an understanding of which techniques are applicable to which problems. Advanced students will also gain tools for the design and analysis of machine learning algorithms.