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 10708-instructor@cs.cmu.edu.