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.
|Jan 14, 2019||Welcome to PGM, Spring 2019!|