Projects

  • Goals & deliverables

  • Logistics

  • Timeline

  • Types of projects

  • Project examples

Goal & deliverables

The deliverables consists in a single .zip file posted on bspace containing:

  • A 6-10 page project report detailing the case study. The report should be written preferably with LaTeX.

  • All the figures (in .pdf format) and matlab files needed to reproduce the results.

  • A README file detailing

    • The names and SID's of the students involved.

    • The contents of the .zip file (names and short description of each file).

  • The name of the .zip file should be of the form <XXX>_EE227ATSp14.zip, where XXX is a tag for the project. Example: GRAPHMOD_EE227ATSp14.zip.

In addition the teams will be asked to present their work in a project session, which will be held during reading week, on Tue May 6.

Logistics

  • Each project involves a team of 3-4 students.

  • Groups should be decided no later than Thursday March 6.

  • Topics should be decided no later than Thursday March 27.

  • There will be a single project grade for each group.

Timeline

  • March 6: teams are formed.

  • March 27: topics are chosen. 1-2 page description of the project is due.

  • April 22: 2-4 page progress report due.

  • May 6: Project Session

  • May 16: all project deliverables are due. Post the .zip file on bspace and turn in a hard copy to 421 SDH between 11am and 12pm.

Types of projects

Projects can have the following different formats:

  • Literature review: The project describes in detail a set of 4-5 papers. This should include reproducing experiments. Example: learning sparse graphical models.

  • Design methodology: the project describes a (possibly new) methodology for addressing a particular design problem. Here the focus is not on algorithms or past work. Example: geometric programming for water distribution networks.

  • Theory: the project examines a theoretical issue related to convex optimization. Example: quality of SDP relaxation for sparse PCA.

  • Algorithms: the project tests various algorithms for solving a particular type of problem. Example: Stochastic vs. accelerated gradient for large-scale logistic regression.

Project examples