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

Course Availability

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 education section.

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