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\title{Project Proposal for 10-605} 

\author{William Cohen}

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\maketitle

\section{Overview of the course project}

The goal of the project is to give you additional experience using
large data for some analytical task.  You should work in a group of
two or three students (if you want to work in a group of one or four,
please clear this with William.)

The major milestones for project-related tasks are:
\begin{itemize}
\item By 12:00pm Wed 3/20: Hand in an \emph{initial proposal}.  The initial
  proposal should describe: the team of students working on this
  project, the data you plan to use, a description of the analysis
  that you plan to perform, and a description of how you plan to
  evaluate your work.

\item By 12:00pm Tues 4/4: Hand in a \emph{final proposal}, revised
  based on comments you will have received from the instructors.

\item By 1:30pm Fri 5/3: Hand in the \emph{final project writeup},
  which will be in a conference-paper format paper 4-8 pages long.
\end{itemize}
We may also allocate 1-2 class periods to a poster session, or talks,
so you can present your work in that format.

\section{The initial proposal}

Your initial proposal should be no more than two pages long, and
should say:
\begin{itemize}
\item Who will be working on the project. Project teams should be two
  or three students; again, if there's a reason that's not possible,
  please talk to William.
\item What dataset or datasets you plan to use.  You should verify
  that the data is publically available (and actually has all the data
  that you expect it to have).  I strongly suggest using a
  previously-published and readily-available dataset for the project.
  If you want work on a novel dataset, that's ok, but {\bf you must
    have finished all data collection by 3/20}, when this initial
  proposal is due.  A short description of the dataset is also
  helpful.
\item What you plan to do, and how you plan to evaluate it.  I
  strongly suggest picking a well-defined problem that has an existing
  baseline method for solving it, and then focusing the project on
  getting the baseline results, and then improving or analyzing the
  method baseline method.
\item What prior research (that you know of) is most relevant.
\item What computational resources you will use.
\end{itemize}

There are some project and dataset ideas posted on the class wiki.

%\newpage
%
%\section{Predicting relationship types with SVO triples\\Sample project}
%
%{\bf Project team:} William W. Cohen and Ni Lao
%
%{\bf Datasets:} We will use two publically available datasets: the
%normalized infobox data from DBPedia, and the subject-verb-object
%triples made available by the NELL project.  The normalized infobox
%data consists of 17M entity pairs that have a particular relationship:
%e.g., branchFrom(Abdominal\_aorta, Thoracic\_aorta) or
%biome(Adirondack\_Park,Boreal\_forest).  There are 1100 distinct
%relationships.  The subject-verb-object triples dataset is on the
%class /afs site, contains 200 million triples, e.g., (``Tom'',
%``teaches'', ``machine\_learning'').
%
%{\bf Plans:} We plan to merge these datasets and create a training
%dataset where each instance corresponds to an entity pair; the label
%of an instance corresponds to the relationship between them; and the
%features are the derived from subject-verb-object triples that match
%the entities.  For instance, if (a,b) = (Abdominal\_aorta,
%Thoracic\_aorta) is an instance that has label ``BranchFrom'', and
%there is are triples like (``surgery'', ``repairs'', ``thoracic
%aorta'') and (``abdominal aorta'', ``enters'', ``heart'') then we
%might construct a features like ``surgery-repairs-b'' or
%``a-enters-heart''.  We will then use these features to predict the
%label using Naive Bayes or other scalable learning methods.
%
%To evaluate this approach, we will use accuracy on hold-out data.  We
%may focus on a subset of the 1100 classes initially.  
%
%{\bf Related work:} 

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