Spring 2016: Network Science
This PhD-level course will cover state-of-the-art models and algorithms for learning and mining large-scale networks.
This course does not have a designated textbook. The readings are assigned in the syllabus.
o Class participation (20%): When reading a paper consider
What is the problem being addressed?
What are the main technical
What are the weaknesses of the
paper, how could they be improved?
What are some of the promising
research questions, and how could they be pursued?
Semester-long class project (80%):
The content and scope of the project will be decided in consultation
with the instructor. The project can be
on the student’s PhD dissertation. At
the end of the semester, each student will present his/her project to the class
and submit a report (at most 10 pages long) in the ACM formatting
guidelines. For guidance on writing
the final report, please see Slide 70 of Eamonn
Keogh's KDD'09 Tutorial on How to do good
research, get it published in SIGKDD and get it cited!
o Eliassi-Rad’s Resources Page
o Mathworks Matlab Tutorials
Taskar's Matlab Tutorial
Review (David Blei, Princeton)
Theory Review (Arian Maleki and Tom Do, Stanford)
o Linear Algebra
Tutorial (C.T. Abdallah, Penn)
Algebra Review and Reference (Zico Kolter and Chuong Do, Stanford)
Data Mining Tutorials (Andrew Moore, Google/CMU)
CS Cheat Sheet (Princeton)
o When emailing me, please begin the subject line with [sp16
o For your class project, you can use whatever programming
language you like.
o Refresh your knowledge of the university's academic
integrity policy and plagiarism.
There is zero-tolerance for cheating!