- Schedule of Classes - November 28, 2022 7:30PM EST
- Course Catalog - November 28, 2022 7:14PM EST
Course information provided by the Courses of Study 2022-2023.
The goal of this course is to introduce data structural and computational models that are indexed by the irregular support of a graph. The graph represents the network that couples the dynamics of many agents, or it can be a more abstract Bayesian graphical model that explains how observations are conditionally dependent. The course will start from introducing basic concepts in graph theory followed by an introduction to random graphs models. This part will be followed by network dynamical models that model the observations from these processes. Bayesian graphical models will be briefly covered as a more general statistical abstraction and computational framework to perform inferences. The course will then introduce the students to the emerging field of graph signal processing, a theory that generalizes digital and image processing to graph signals.
When Offered Fall.
Permission Note Enrollment limited to: Cornell Tech students.
Prerequisites/Corequisites Prerequisite: linear algebra, probability theory, basic python or MATLAB programming skills.
- Students will be able to identify the type of data that are amenable to be the outcome of network dynamical models or Bayesian graphical network models exemplified in the course or a generalization of the ones covered.
- Students will be able to understand the difference between these kinds of multivariate data compared to time-series or images.
- Students will be able to analyze the data to predict and infer data trends, for a given model.
- Students will be able to analyze the data to learn the latent network structure and system parameters.
- Students will be able to demonstrate and document the data analysis performance on synthetic and on real data.
Regular Academic Session. Combined with: ORIE 5735
Credits and Grading Basis
3 Credits Stdnt Opt(Letter or S/U grades)
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