IS 542 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
IS542ATue, 9:30 am - 12:20 pmRoom 242On-CampusThis is a required course for the MSIM degree.
- Jill Naiman
- 16 weeks
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
Specify, estimate and evaluate elementary parametric and non-parametric statistical models.