IS 507 AO - Data, Stat, Info
An introduction to statistical and probabilistic models as they pertain to quantifying information, assessing information quality, and principled application of information to decision making, with focus on model selection and gauging model quality. The course reviews relevant results from probability theory, parametric and non-parametric predictive models, as well as extensions of these models for unsupervised learning. Applications of statistical and probabilistic models to tasks in information management (e.g. prediction, ranking, and data reduction) are emphasized. Course Information: 4 graduate hours. No professional credit. Prerequisite: Graduate standing.
Graduate student questions may be sent to firstname.lastname@example.org.
Academic Program Restrictions:
Option 1Number of Required Visit(s): 0
Course Level: Graduate
Term(s): Fall , Spring