CS 228: Probabilistic Graphical Models

Announcements

• Important announcements will be posted on Piazza.

General Information

• Time/Location:
  • Lectures: Tue/Thu 9:00-10:20am, Gates B1.
  • Office Hours: See calendar
  • Final Exam: March 21, 8:30-11:30am. Location: TBA.

• Instructor: Stefano Ermon

• Course Assistants:
  • Nishith Khandwala (nishith@stanford.edu)
  • Jonathan Kuck (kuck@stanford.edu)
  • Ya Le (yle@stanford.edu)
  • Alisha Rege(amr6114@stanford.edu)
  • Stephanie Wang (steph17@stanford.edu)
  • Moosa Zaidi(mzaidi@stanford.edu)

• Calendar: Click here for detailed information of all lectures, office hours, and due dates.

• Contact: Please use Piazza for all questions related to lectures and coursework. For SCPD students, please email scpdsupport@stanford.edu or call 650-741-1542.

Coursework

• Course Description:

  • Probabilistic graphical models are a powerful framework for representing complex domains using probability distributions, with numerous applications in machine learning, computer vision, natural language processing and computational biology. Graphical models bring together graph theory and probability theory, and provide a flexible framework for modeling large collections of random variables with complex interactions. This course will provide a comprehensive survey of the topic, introducing the key formalisms and main techniques used to construct them, make predictions, and support decision-making under uncertainty.

• • The aim of this course is to develop the knowledge and skills necessary to design, implement and apply these models to solve real problems. The course will cover: (1) Bayesian networks, undirected graphical models and their temporal extensions; (2) exact and approximate inference methods; (3) estimation of the parameters and the structure of graphical models.

• Prerequisites: Students are expected to have background in basic probability theory, statistics, programming, algorithm design and analysis.

• Required Textbook: Probabilistic Graphical Models: Principles and Techniques by Daphne Koller and Nir Friedman. MIT Press.

• Lecture notes: Lecture notes are available here and will be periodically updated throughout the quarter.

• Further Readings:

  • Modeling and Reasoning with Bayesian networks by Adnan Darwiche.

  • Pattern Recognition and Machine Learning by Chris Bishop.

  • Machine Learning: a Probabilistic Perspective by Kevin P. Murphy.

  • Information Theory, Inference, and Learning Algorithms by David J. C. Mackay. Available online.

  • Bayesian Reasoning and Machine Learning by David Barber. Available online.

  • Graphical models, exponential families, and variational inference by Martin J. Wainwright and Michael I. Jordan. Available online

  
  
• Grading Policy:

  • Homeworks (70%): There will be five homeworks with both written and programming parts. Each homework is centered around an application and will also deepen your understanding of the theoretical concepts. Homeworks will be posted on Piazza.

  • Final Exam (30%): March 21, 8:30-11:30am

  • Piazza: You will be awarded with up to 3% extra credit if you answer other students' questions in a substantial and helpful way, or contribute to the lecture notes with pull requests.

  
  
• Assignments:

  • Written Assignments: Homeworks should be written up clearly and succinctly; you may lose points if your answers are unclear or unnecessarily complicated. You are encouraged to use LaTeX to writeup your homeworks (here is a template), but this is not a requirement.

  • Collaboration Policy and Honor Code: You are free to form study groups and discuss homeworks and projects. However, you must write up homeworks and code from scratch independently without referring to any notes from the joint session. You should not copy, refer to, or look at the solutions in preparing their answers from previous years' homeworks. It is an honor code violation to intentionally refer to a previous year's solutions, either official or written up by another student. Anybody violating the honor code will be referred to the Office of Judicial Affairs.

  
  
• Submission Instructions:

  • We will be using the GradeScope online submission system. All students (non-SCPD and SCPD) should submit their assignments electronically via GradeScope. Students can typeset or scan their homeworks.

  • To register for GradeScope,

    • Create an account on GradeScope if you don't have one already.
    • Join CS228 course using Entry Code 9BPRJZ
    • Fill in this form.

  • Here are some tips for submitting through Gradescope.

  • Late Homework: You have 6 late days which you can use at any time during the term without penalty. For a particular homework, you can use only two late days. Once you run out of late days, you will incur in a 25% penalty for each extra late day you use. Each late homework should be clearly marked as "Late" on the first page.

  • Regrade Policy: You may submit a regrade request if you believe that the course staff made an error in grading. Any regrade requests should be submitted through Gradescope within one week of receiving your grade. Please try to be as specific as possible with your regrade request.

Syllabus (tentative, periodically updated throughout the quarter)

• Week	Date	Topic	Readings	Assignments
  1	Jan. 9-11	Introduction, Probability Theory, Bayesian Networks	Chapters 1-3	Homework 1 released. Due January 23.
  2	Jan. 16-18	Undirected models	Chapter 4
  3	Jan. 23-25	Learning Bayes Nets	Chapters 16, 17	Homework 2 released.
  4	Jan. 30 - Feb 1	Exact Inference; Message Passing	Chapters 9, 10	Homework 3 released.
  5	Feb. 6-8	Sampling	Chapter 12
  6	Feb. 13-15	MAP Inference; Structured prediction	Chapter 13;	Homework 4 released.
  7	Feb. 20-22	Parameter Learning	Chapter 20; Chapter 19
  8	Feb. 27 - Mar. 1	Bayesian Learning; Structure Learning	Chapter 17; Chapter 18	Homework 5 released.
  9	Mar. 6-8	Exponential families; variational inference	Chapter 8; Chapter 11; Graphical models, exponential families, and variational inference (Section 3)
  10	Mar. 13-15	Advanced topics and conclusions

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Many thanks to David Sontag, Adnan Darwiche, Vibhav Gogate, and Tamir Hazan for sharing material used in slides and homeworks.

Other Resources

There are many software packages available that can greatly simplify the use of graphical models. Here are a few examples:

• SamIam
• BNT: Bayes Net Toolbox (Matlab)
• libDAI (C++)
• OpenGM