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toronto and hackathon

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Edgar Bering 2010-11-03 15:33:37 -04:00
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events.xml
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<eventdefs>
<!-- Fall 2010 -->
<eventitem date="2010-11-13" time="12:30 PM" room="Outside DC" title="CSC Invades Toronto">
<short><p>The CSC is going to Toronto to visit UofT's <a href="http://cssu.cdf.toronto.edu/">CSSU</a>, see what they do, and have beer with them.
If you would like to come along, please come by the office and sign up. The cost for the trip is $2 per member.</p></short>
</eventitem>
<eventitem date="2010-11-05" time="07:00 PM" room="C&D Lounge (MC3002)" title="Hackathon">
<short><p>Come join the CSC for a night of code, music with only 8 bits, and comradarie. We will be in the C&amp;D Lounge from 7pm until 7am working on personal projects, open source projects, and whatever else comes to mind. If you're interested in getting involved in free/open source development, some members will be on hand to guide you through the process.
</p></short>
<abstract><p>Come join the CSC for a night of code, music with only 8 bits, and comradarie. We will be
in the C&amp;D Lounge from 7pm until 7am working on personal projects, open source projects, and
whatever else comes to mind. If you're interested in getting involved in free/open source development,
some members will be on hand to guide you through the process.
</p></abstract>
</eventitem>
<eventitem date="2010-10-26" time="04:30 PM" room="MC4040" title="Analysis of randomized algorithms via the probabilistic method">
<short><p>In this talk, we will give a few examples that illustrate the basic method and show how it can be used to prove the existence of objects with desirable combinatorial properties as well as produce them in expected polynomial time via randomized algorithms. Our main goal will be to present a very slick proof from 1995 due to Spencer on the performance of a randomized greedy algorithm for a set-packing problem. Spencer, for seemingly no reason, introduces a time variable into his greedy algorithm and treats set-packing as a Poisson process. Then, like magic, he is able to show that his greedy algorithm is very likely to produce a good result using basic properties of expected value.