Announcement
- <2020-12-30 Wed> Welcome! Please stay tuned for more updates. We
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COVID-19.
About
- Syllabus:
PDF.
- Instructor: Prof. Fang Song
- Email: fsong “AT” pdx.edu. Start your email subject line
with “w21-5610-pgm”.
- Lectures: TR 12:00 - 13:15, remotely via Zoom.
- Office hours: F 10 - 11am, remotely via zoom.
- Zoom links: Check
“Schedule” page. Also
check “PSU Classes” calendar under your PSU Google Suite.
- Overview: This course will introduce probabilistic graphical
models and investigate emerging applications. Graphical models
bring together graph theory and probability theory, and provide a
flexible framework for modeling probabilistic distributions with
complex dependencies. This course will cover the key formalisms and
main techniques to construct them and to solve optimization and
learning problems.
- Prerequisite: You must be comfortable with reading and writing
mathematical proofs. Familiarity with algorithm analysis, linear
algebra, and basic probability theory is essential.
- Text: No required text. See Resource
page for suggested texts
and additional useful materials.