This course covers the foundations of statistical machine learning. The focus is on probabilistic and statistical methods for prediction and clustering in high dimensions. Topics covered include linear and logistic regression, SVMs, PCA and dimensionality reduction, EM and HMMs, and deep learning. Elementary probability, calculus, and linear algebra. Basic programming experience.
CIS5970 - Master's Thesis Research
For students working on an advanced research leading to the completion of a Master's thesis.
CIS5990 - Master's Indep Study
For master's students studying a specific advanced subject area in computer and information science. Involves coursework and class presentations. A CIS 5990 course unit will invariably include formally gradable work comparable to that in a CIS 500-level course. Students should discuss with the faculty supervisor the scope of the Independent Study, expectations, work involved, etc.
CIS6200 - Adv Top in Mach Learning
This course covers a variety of advanced topics in machine learning, such as the following: statistical learning theory (statistical consistency properties of surrogate loss minimizing algorithms); approximate inference in probabilistic graphical models (variational inference methods and sampling-based inference methods); structured prediction (algorithms and theory for supervised learning problems involving complex/structured labels); and online learning in complex/structured domains. The precise topics covered may vary from year to year based on student interest and developments in the field.
CIS7000 - Cis-Topics
One time course offerings of special interest. Equivalent to a CIS 5XX level course.
CIS8000 - PhD Special Topics
One-time course offerings of special interest. Equivalent to CIS seminar course. Offerings to be determined.
CIS9990 - Master's Thesis
For students working on an advanced research program leading to the completion of master's thesis requirements.