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

Recent syllabus

Scheduled Offerings

  • Fall 2018

    • IS542A
      Tue, 9:30 am - 12:20 pm
      Room 126
      On-Campus
      This is a required course for the MSIM degree.
      Instructor
      Vetle Torvik
      CRN
      68856
      Length
      16 weeks
    • IS542B
      Mon, 9:00 am - 11:50 am
      Room 126
      On-Campus
      This is a required course of the MSIM Degree.
      Instructor
      David Dubin
      CRN
      68916
      Length
      16 weeks
    • IS542C
      Thu, 1:00 pm - 3:50 pm
      Room 131
      On-Campus
      This is a required course of the MSIM Degree.
      Instructor
      David Dubin
      CRN
      70384
      Length
      16 weeks
  • Spring 2019

    • IS542A
      Tue, 9:30 am - 12:20 pm
      Room 126
      On-Campus
      This is a required course for the MSIM degree.
      Instructor
      Jill Naiman
      CRN
      67383
      Length
      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

    modeling information.

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