Class lectures: Mondays and Wednesdays 10:30-11:50am in Wean Hall 5409  (starting Sept 8)

Recitations: Thursdays 5:00-6:20, Wean Hall 5409

Special Recitations: Optional recitations for advanced topics will be announced

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 general 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, dynamic Bayesian networks, and relational models; 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.

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

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Homework policy

Important Note: As we often reuse problem set questions from previous years, or problems covered by papers and webpages, we expect the students not to copy, refer to, or look at the solutions in preparing their answers. Since this is a graduate class, we expect students to want to learn and not google for answers. The purpose of problem sets in this class is to help you think about the material, not just give us the right answers. Therefore, please restrict attention to the books mentioned on the webpage when solving problems on the problem set. If you do happen to use other material, it must be acknowledged clearly with a citation on the submitted solution.

Collaboration policy

Homeworks will be done individually: each student must hand in their own answers. In addition, each student must write their own code in the programming part of the assignment. 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. In preparing your own writeup, you should not refer to any written materials from a joint study session. You also must indicate on each homework with whom you collaborated. The final project may be completed individually or in teams of two students.

Late homework policy

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, and we will consider your request. Please note that regrading of a homework may cause your grade to go up or down.

Final project

For project milestone, roughly half of the project work should be completed. A short, graded write-up will be required, and we will provide feedback.

Note to people outside CMU

Feel free to use the slides and materials available online here. If you use our slides, an appropriate attribution is requested. Please email the instructors with any corrections or improvements.