140.644.01 STATISTICAL MACHINE LEARNING: METHODS, THEORY, AND APPLICATIONS
Introduces statistical and computational foundations of modern statistical machine learning. Acquaints students with methods to evaluate statistical machine learning models defined in terms of algorithms or function approximations using basic coverage of their statistical and computational theoretical underpinnings. Topics covered include: regression and classification, tree-based methods, overview of supervised learning theory, support vector machines, kernel methods, ensemble methods, clustering, visualization of large datasets and graphical models. Example applications include cancer prognosis from microarray data, graphical models for data visualization and decision making.
Upon successfully completing this course, students will be able to describe methods to evaluate statistical machine learning models.
- Monday 10:30 - 11:50
- Wednesday 10:30 - 11:50
Students are expected to be familiar with the following topics to comfortably complete this class. If you do not know these topics, it is your responsibility to do background reading to make sure you understand these concepts.