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 announcedMany 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.
- Class announcements will be broadcasted using a group
- Please subscribe the 10708-announce list page.
- For changes (incl. additions or removal) to your membership in the course list, please make changes directly via the list administration page.
- We have a discussion group where you can post questions, discuss issues, and interact with fellow students. Please join the group and check in often:
- Primary: Daphne Koller and Nir Friedman, Bayesian Networks and Beyond, in preparation. These chapters are part of the course reader, and can be purchased from Michelle Martin in Wean 4619.
- Secondary: M. I. Jordan, An Introduction to Probabilistic Graphical Models, in preparation. Copies of selected chapters will be made available.
- Homeworks (5 assignments 50%)
- Final project (30%)
- Final exam (20%)
- We don't know for sure yet whether we will be able to
allow auditors. If you are considering auditing, you should attend the
first class. In any case, students wishing to audit must register to
audit the class. To satisfy the auditing requirement, you must either:
- Do *two* homeworks, and get at least 75% of the points in each; or
- Take the final, and get at least 50% of the points; or
- Do a class project and do *one* homework, and get at
least 75% of the points in the homework
- 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 us an email saying that you will be auditing the class and what you plan to do.
- If you are not a student and want to sit in the class, please get authorization from the instructors.
Homework policyImportant 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 policyHomeworks 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
- Homeworks are due at the begining of class, unless otherwise specified.
- You will be allowed 3 total late days without penalty
the entire semester. For instance, you may be late by 1 day on three
different homeworks or late by 3 days on one homework. Each late day
corresponds to 24 hours or part thereof. Once those days are used, you
will be penalized according to the policy below:
- 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 all of the 5 homeworks, even if for zero credit, in order to pass the course.
- Turn in all late homework assignments to Michelle (Wean Hall 4619) .
Homework regrades policyIf 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.
- Project proposal due date: Oct 8th
- Graded milestone due date: Nov 6th (by 5pm, start of recitation) (20% of project grade)
- Poster session: Dec 1st 3-6pm in the NSH Atrium (20% of project grade)
- Paper due date: Dec 3rd by 3pm (via electronic submission to the instructors list) (60% of project grade)