IS 507 Data, Statistical Models, and Information

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.

  • Required core course – MS in Information Management

Textbooks and Course Materials

  • Articulate the role of marginal, joint, and conditional probability in modeling

    processes involving information.

  • Select, parameterize, and compare probability distributions as vehicles for

    modeling information.

  • Specify, estimate and evaluate elementary parametric and non-parametric statistical models.