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CS 228

Probabilistic Graphical Models: Principles and Techniques

  • winter
  • 2018-2019

3 - 4 units

Letter or Credit/No Credit

Probabilistic graphical modeling languages for representing complex domains, algorithms for reasoning using these representations, and learning these representations from data. Topics include: Bayesian and Markov networks, extensions to temporal modeling such as hidden Markov models and dynamic Bayesian networks, exact and approximate probabilistic inference algorithms, and methods for learning models from data. Also included are sample applications to various domains including speech recognition, biological modeling and discovery, medical diagnosis, message encoding, vision, and robot motion planning. Prerequisites: basic probability theory and algorithm design and analysis.

CS 228 is useful for

Sections

winter
  • LEC

    • Tuesday Thursday 9:00:00 AM - 10:20:00 AM with Stefano Ermon

      May be taken for 3 units by graduate students.

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