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.
- Time: Monday/Wednesday 12:00-1:20 pm
- Location: Posner Hall 152
- Discussion: Piazza
- HW submission: Gradescope
- Online lectures: The lectures will be live-streamed through Panopto, recorded, and made available on YouTube.
- Contact: Students should ask all course-related questions on Piazza, where you will also find announcements. For external enquiries, personal matters, or in emergencies, you can email us at firstname.lastname@example.org.
|Apr 25, 2019||Project presentation guidelines have been posted. Please submit a PDF version of your posters on Gradescope by 11:59 PM on Apr 29. Poster session will take place on Tuesday, Apr 30, 3-5 pm.|
|Apr 8, 2019||Homework 4 has been released. Due: 11:59 PM on Apr 24. Submit on Gradescope.|
|Mar 22, 2019||Project midway report submission form is up on Gradescope and due by 11:59 PM on Mar 29.|
|Mar 18, 2019||Homework 3 has been released. Due: 11:59 PM on Apr 3. Submit on Gradescope.|
|Feb 22, 2019||Homework 2 has been released. Due: 11:59 PM on Mar 11. Submit on Gradescope.|