10-708 – Probabilistic Graphical Models

Many of the problems in artificial intelligence, statistics, computer systems, computer vision, natural language processing, and computational biology, among many other fields, can be viewed as the search for a coherent global conclusion from local information. The probabilistic graphical models framework provides an unified view for this wide range of problems, enables efficient inference, decision-making and learning in problems with a very large number of attributes and huge datasets. This graduate-level course will provide you with a strong foundation for both applying graphical models to complex problems and for addressing core research topics in graphical models.

• Instructor: Eric P. Xing (epxing@cs)

• Time: MW 12:00-1:20pm

• Location: Wean 7500

• Office Hours: Mon 1:30-2:30pm GHC 8101

• Piazza: https://www.piazza.com/cmu/spring2020/10708

• Gradescope: https://www.gradescope.com/courses/80181

• TAs (email, office hours):

  • Xun Zheng (xzheng1@andrew, Fri 4-5pm GHC 8013)

  • Ben Lengerich (blengeri@andrew, Thu 10-11am GHC 9005)

  • Haohan Wang (haohanw@andrew, Fri 5-6pm, GHC 5507)

  • Yiwen Yuan (yiweny@andrew, Tue 1:50-2:50pm, outside GHC 8011)

  • Xiang Si (xsi@andrew, Wed 2-3pm, GHC Citadel Commons)

  • Junxian He (junxian1@andrew, Mon 4-5pm GHC 6603)