Description and Objective:
Inferring the values of independent variables becomes exponentially more difficult as the number of variables increases, so it is only possible if we can use structural knowledge. One way to encode this knowledge is through graphical models. This generalizes the notion of Hidden Markov Models, which have found applications ranging from speech recognition to protein sequence matching. Graphical models have much wider applications, including protein networks, machine vision, decoding error-correcting codes, etc. We will use the book Probabilistic Graphical Models by Koller and Friedman to study the properties and applications of these models, and whether they can be used to infer causality rather than just correlation. This course relates to Machine Learning and Artificial Intelligence, but does not assume them as prerequisites. We will review basic probability, but it will be helpful to have had prior experience working in a probabilistic setting.

Prerequisites: Comp 15 and either MATH 22 or familiarity with basic probability theory.

Text:
The textbook for the course is Probabilistic Graphical Models. Daphne Koller and Nir Friedman, MIT Press(2009) ISBN: 978-0-262-01319-2

Instructor:
Anselm Blumer
ablumer (at) cs dottufts dot edu
Halligan Hall, Room 214
Office Hours: Mondays 11-12 and 5-6, Tuesdays 12-1 and by appointment.
Home page

Communication:
Students are encouraged to communicate frequently with the instructor and TA regarding any issues with the course. Students are encouraged to use email and office hours frequently. Any announcements regarding the course will be made via the course webpage or in class so be sure to check it frequently and be sure to get material for any class you miss.

Homework:
Homework will be assigned regularly in the course. While reading assignments will not be directly assigned it is important that students use the textbook to supplement their understanding of the material presented in the lecture. The majority of the assignments will be written assignments due on Wednesdays at the beginning of class on the due date specified. This work can be handwritten with the assumption that these assignments are legible. (A student may be asked to type their assignments if grading is not possible.)

Late Homeowork:
Because of the size of the class and the amount of homework 15% of the total number of points for the assignment will be deducted daily. No homework will be accepted after one week.

Exams:
There will be no exams.

Grade Calculation:
95% Homework
5% Class participation

Feedback:
Your thoughts and concerns on this course are important. You are encouraged to give feedback to the instructor throughout the term. As always students will be asked to fill out a course evaluation at the end of the term.

Academic Misconduct:
Students should read the Tufts brochure on academic integrity located at: http://uss.tufts.edu/studentaffairs/judicialaffairs/Academic%20Integrity%2010-11.pdf

A few highlights are presented to emphasize importance:

Absolute adherence to the code of conduct is demanded of the instructor, teaching fellow, and students. This means that no matter the circumstance any misconduct will be reported to Tufts University.

While students are encouraged to discuss course materials, no collaboration is allowed on homework. Specifically you may discuss assignments and projects verbally, but must write up or work on the computer alone. In addition any discussion should be documented. An example on the homework would be "Thanks to Ray for helping me understand Kolmogorov complexity." Another important example is citing a source, this could be "This information was adapted from www.boston.com"

While computers enable easy copying and collaboration both with other students and materials from the Internet, it is possible to use these same computers to detect plagiarism and collaboration.

If any student does not understand these terms or any outlined in The Academic Code of Conduct it is his/her responsibility to talk to the instructor.