Probabilistic Graphical Models: Principles and Techniques


Stanford School of Engineering



Learn important probabilistic modeling languages for representing complex domains and how the graphic models extend to decision making. Use ideas from discrete data structures in computer science to efficiently encode and manipulate probability distributions over high-dimensional spaces. Apply the basics of the Probabilistic Graphical Model representation and learn how to construct them, using both human knowledge and machine learning techniques to reach conclusions and make good decisions under uncertainty.



Basic probability theory and algorithm design and analysis.

Topics include

  • Bayesian and Markov networks
  • Exact and approximate probabilistic inference algorithms
  • Speech recognition
  • Biological modeling and discovery
  • Message encoding
  • Medical diagnosis
  • Robot motion planning

Note on Course Availability

This course is typically offered Winter quarter.

The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate certificate homepage.

019 Winter 2019-20 Online

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Dates:January 6 - March 13, 2020
Days: Tue, Thu
Units: 3.00-4.00
Instructors: Stefano Ermon
Delivery Option:
For Credit $5,200.00 ?


Enrollment Dates: October 27 to December 9, 2019

Note: A wait list will open after this course reaches its capacity for enrollments. In the case that a spot becomes available, Student Services will contact you. Make sure you have submitted your NDO application and required documents to be considered.

Computer Science Department Requirement
Students taking graduate courses in Computer Science must enroll for the maximum number of units and maintain a B or better in each course in order to continue taking courses under the Non Degree Option.

This course may not currently be available to learners in some states and territories.