Difference between revisions of "Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2013"

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* 9am, Tuesday, May 7.  '''Project writeups due'''.  Submit a paper to Blackbook in PDF in the [http://icml.cc/2013/author-instructions/ ICML 2013 format] (minimum 5 pp, up to 8pp double column), except, of course, do not submit it anonymously.
 
* 9am, Tuesday, May 7.  '''Project writeups due'''.  Submit a paper to Blackbook in PDF in the [http://icml.cc/2013/author-instructions/ ICML 2013 format] (minimum 5 pp, up to 8pp double column), except, of course, do not submit it anonymously.
** ''Extended from previous deadline of Fri May 3.''
+
** ''Note: this is extended from previous deadline of Fri May 3---but I can't give any further extensions!''  Your project report should discuss
 +
*** The problem you're trying to solve, and why it's important and/or interesting.
 +
*** Related work, especially any related work that you're building on.
 +
*** The data that you're working with.
 +
*** The methods that you're using (in some detail - even if these are off-the-shelf methods, I want to know that you understand them)
 +
*** The experiments you did, the metrics you used to evaluate them, and the results.
 +
*** What was learned from the experiments (the conclusions).
 +
** You should think of this as an exercise in writing a conference-style paper: so try and write in that style.  (Of course, your work doesn't need to advance the state-of-the-art in machine learning, or be highly novel, but it should be well-described.)

Revision as of 11:49, 17 April 2013

This is the syllabus for Machine Learning with Large Datasets 10-605 in Spring 2013.

January

February

March

April and May

May

  • 9am, Tuesday, May 7. Project writeups due. Submit a paper to Blackbook in PDF in the ICML 2013 format (minimum 5 pp, up to 8pp double column), except, of course, do not submit it anonymously.
    • Note: this is extended from previous deadline of Fri May 3---but I can't give any further extensions! Your project report should discuss
      • The problem you're trying to solve, and why it's important and/or interesting.
      • Related work, especially any related work that you're building on.
      • The data that you're working with.
      • The methods that you're using (in some detail - even if these are off-the-shelf methods, I want to know that you understand them)
      • The experiments you did, the metrics you used to evaluate them, and the results.
      • What was learned from the experiments (the conclusions).
    • You should think of this as an exercise in writing a conference-style paper: so try and write in that style. (Of course, your work doesn't need to advance the state-of-the-art in machine learning, or be highly novel, but it should be well-described.)