Course Description

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, enabling 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.

The class will cover three aspects: The core representation, including Bayesian and Markov networks, and dynamic Bayesian networks; probabilistic inference algorithms, both exact and approximate; and, learning methods for both the parameters and the structure of graphical models. Students entering the class should have a pre-existing working knowledge of probability, statistics, and algorithms, though the class has been designed to allow students with a strong numerate background to catch up and fully participate.

It is expected that after taking this class, the students should have obtain sufficient working knowledge of multi-variate probablistic modeling and inference for practical applications, should be able to fomulate and solve a wide range of problems in their own domain using GM, and can advance into more specialized technical literature by themselves.

Students are required to have successfully completed 10701/15781, or an equivalent class.

Textbook

This class has a required textbook:

We will also be using excerpts from the following work, which you do not need to purchase:

  • M. I. Jordan, An Introduction to Probabilistic Graphical Models, in preparation. Copies of chapters will be made available.

Grading

The class requirements include scribing lectures, brief lecture summaries, problem sets, and a project. This is a PhD level class, and by the end of this class you should have an understanding of the basic methodologies in probabilistic graphical models, and be able to use them to solve real problems of modest complexity. The grading breakdown is as follows:

  • Homework (4 assignments, 40%)
  • Scribe Duties (10%)
  • Lecture Summaries (10%)
  • Final project (40%)

Note that this class does not have any exams.

Scribe duties

Each student is required to scribe for a small number of lectures. Most lectures will have at least 2 students acting as scribes, and they should work as a team. During your assigned lectures, you are to take detailed lecture notes in collaboration with your fellow scribes. After the lecture, the scribe team is to convert their notes into LaTeX format (we will provide a template for your use). These notes should be about 2-3 pages long, and must be submitted to the instructors at most 1 week after the lecture. We only require one set of notes from the scribe team. The instructors will then audit your notes, and post them to the class page for everyone's benefit.

As long as your scribe notes are of sufficient standard, you will be awarded full credit for scribe duties. If your notes have errors or are otherwise not up to standard, we will inform you and give you a chance to correct them. You will receive zero credit if you fail to submit your notes.

You are encouraged to consult with other students before submitting your scribe notes to us. Our objective is to ensure that you understand the class material!

Lecture Summaries

The required readings for each lecture are compulsory. At the beginning of each lecture, you are to submit a 1-paragraph summary of the readings for that lecture. You must turn in your summary as a hardcopy; we will not accept email. If you need to miss a lecture for a good reason, you must let us know in advance, and we still require you to turn in a summary via email.

Your summary should be kept high-level, and should focus on the main point of the readings (i.e. avoid complicated math). As long as your summary is reasonable, you will be given full credit.

Homework resources and collaboration policy

Homeworks may contain material that has been covered by papers and webpages. Since this is a graduate class, we expect students to want to learn and not Google for answers.

Homeworks will be done individually: each student must hand in their own answers. It is acceptable, however, for students to collaborate in figuring out answers and helping each other solve the problems. We will be assuming that, as participants in a graduate course, you will be taking the responsibility to make sure you personally understand the solution to any work arising from such collaboration. You also must indicate on each homework with whom you collaborated.

Late homework policy

  • You will be allowed 2 total late days without penalty for the entire semester.  You may be late by 1 day on two different homeworks or late by 2 days on one homework. Once those days are used, you will be penalized according to the following policy:
    • Homework is worth full credit at the beginning of class on the due date.
    • It is worth half credit for the next 48 hours.
    • It is worth zero credit after that.
  • You must turn in at least n-1 of the n homeworks, even if for zero credit, in order to pass the course.
  • Turn in all late homework assignments to Michelle Martin.

Homework regrades policy

If you feel that we have made an error in grading your homework, please turn in your homework with a written explanation to  Michelle Martin and we will consider your request. Please note that regrading of a homework may cause your grade to go up or down.

 

Note to people outside CMU

Please feel free to reuse any of these course materials that you find of use in your own courses.  We ask that you retain any copyright notices, and include written notice indicating the source of any materials you use.

 

Auditing

To satisfy the auditing requirement, you must either:

  • Do *two* homeworks, and get at least 75% of the points in each; or
  • Do a class project
    • Like any class project, it must address a topic related to machine learning and you must have started the project while taking this class (can't be something you did last semester). You will need to submit a project proposal with everyone else, and present a poster with everyone. You don't need to submit a milestone or final paper. You must get at least 80% on the poster presentation part of the project.
Please, send the instructors an email saying that you will be auditing the class and what you plan to do.
© 2009 Eric Xing @ School of Computer Science, Carnegie Mellon University
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