IS 507
Fall 2025 All Classes
All Classes
Data, Statistical Models and Information
Credit: 4 hours.
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.
Prerequisite: Graduate standing.
This course satisfies the General Education Criteria in
Fall 2022 for:
Section Status updates every 10 minutes.
| CRN | Type | Section | Time | Day | Location | Instructor | Section Details | |
|---|---|---|---|---|---|---|---|---|
|
68976
|
Lecture-Discussion
|
AC
|
10:00AM
-12:50PM
|
M
|
101 Transportation Building
|
Duvvuru, J
Wang, Y Yu, Y Zhu, L |
|
|
|
70324
|
Lecture-Discussion
|
BC
|
9:30AM
-12:20PM
|
T
|
3025 Campus Instructional Facility
|
Li, Y
Wang, H |
|
|
|
73151
|
Lecture-Discussion
|
CC
|
12:30PM
-3:20PM
|
T
|
126 Grad Sch of Lib & Info Science
|
Nam, A
Salami, M Torvik, V |
|