Homework should be turned in at the beginning of the class on the day it is due. If you are taking late day(s), please send your homework as an email to the TA and also submit a hard copy next time in class. Note the number of late days you used on the top front of the first page of your homework.

We ask that you electronically submit (using email to the TA) all code that was used to complete the assignment. Use the subject line [HWx code] where x is the homework number.

There will be a midterm and a final exam. Note: Both the midterm and the final will be open book, notes, papers, etc., but you are not allowed to use a computer. The tentative dates are posted above, the finalized dates will be announced during the semester.

Your class project is an opportunity for you to explore an interesting machine learning problem of your choice in the context of a real-world data set. Below, you will find some project ideas (will be posted), but the best idea would be to combine machine learning with problems in your own research area. Your class project must be about new things you have done this semester; you can't use results you have developed in previous semesters.

Projects can be done by you as an individual, or in teams of two students. The course TA will consult with you on your ideas, but of course the final responsibility to define and execute an interesting piece of work is yours. Your project will be worth 30% of your final class grade, and will have two main deliverables (in addition to a proposal):

There are four deliverables :

• Project proposal (3% of the course grade)
• Project milestone report (7% of the course grade) (5 pages maximum, including references) describing the results of your first experiments by the milestone due date (see above). Note that, as with any conference, the page limits are strict. Papers over the limit will not be considered.
  
• Final project writeup (10% of the course grade)  preferably in ACM format (8 pages maximum, 4 pages minimum, including references; page limit is strict)
• Final project presentation (in-class)(10% of the course grade)

You must turn in a brief project proposal (1-page maximum) on the due date (see above), in class. A list of suggested projects and data sets are posted below. Read the list carefully. You are encouraged to use one of the suggested data sets, because we know that they have been successfully used for machine learning in the past. If you prefer to use a different data set, we will consider your proposal, but you must have access to this data already, and present a clear proposal for what you would do with it.

Project proposal format: Proposals should be one page maximum. Include the following information:

• Your name and SBU ID on top of the page
• Project title
• Data set
• Project idea.This should be approximately two paragraphs.
• Software you will need to write.
• Papers to read. Include 1-3 relevant papers. You will probably want to read at least one of them before submitting your proposal.
• Teammate: Will you have a teammate? If so, whom? Maximum team size is two students.
• What will you complete by the project milestone due date? Experimental results of some kind are expected here.

• Think of this as an oral version of your final project writeup.
• Present your work in a meaningful and interesting flow (eg, motivation, problem definition, proposed methods, results and their interpretation).
• Make sure to include enough technical details and background of your proposed methods (similar to a conference talk).
• See here and here for some how-to on giving a good/bad talk.
• Be prepared to ask (tough) questions to other project groups.
• We will spend 2 lectures on project talks. Depending on the number of project groups, each group will be given 5-10 minutes including questions.

Project Final Writeup:

Course staff will use the following guidelines when grading your final project reports. Keep in mind however, that if there is a good reason why your project does not match the rubric below, we will take that into consideration when grading your report. For example, we recognize that purely theoretical or data analysis projects may not fit the rubric below perfectly, and that depending on your project you may want to swap the ordering of certain sections. But hopefully all projects can be roughly mapped to the criteria below.

• Introduction/Motivation/Problem Definition (15%)
  What is it that you are trying to solve/achieve and why does it matter?
• Prior Work (10%)
  How does your project relate to previous work? Please give a short summary on each paper you cite and include how it is relevant.
• Model/Algorithm/Method (30%)
  This is where you give a detailed description of your primary contribution. It is especially important that this part be clear and well written so that we can fully understand what you did.
• Results and findings (35%)
  How do you evaluate your solution to whatever empirical, algorithmic or theoretical question you have addressed and what do these evaulation methods tell you about your solution? It is not so important how well your method performs but rather how interesting and clever your experiments and analysis are. We are interested in seeing a clear and conclusive set of experiments which successfully evaluate the problem you set out to solve. Make sure to interpret the results and talk about what we can conclude and learn from your evaluations. Even if you have a theoretical project you should have something here to demonstrate the validity or value of your project (eg., proofs or runtime analysis).
• Style and writing (10%)
  Overall writing, grammar, organization, figures and illustrations.

You are suggested to use the ACM format to write your project reports (8 pages maximum, 4 pages minimum, including references; this page limit is strict).

Right here.