Description of Course

Probabilistic graphical modeling and inference is a powerful modern approach to representing the combined statistics of data and models, reasoning about the world in the face of uncertainty, and learning about it from data. It cleanly separates the notions of representation, reasoning, and learning. It provides a principled framework for combining multiple sources of information, such as prior knowledge about the world, with evidence about a particular case in observed data. This course will provide a solid introduction to the methodology and associated techniques, and show how they are applied in diverse domains ranging from computer vision to computational biology to computational neuroscience.

Textbooks

The following textbook will be used for reading assignments. An electronic copy is available via the UA library webpage (NetID login required):

Course Management

D2L: https://d2l.arizona.edu/d2l/home/1135240
Piazza: https://piazza.com/arizona/spring2022/csc535

Instructor and Contact Information:

Instructor: Jason Pacheco, GS 724, Email: pachecoj@cs.arizona.edu
Office Hours: Fridays, 3-5pm (Zoom via D2L Calendar)
Instructor Homepage: http://www.pachecoj.com