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<p> Thanks for using Virtual Labs. Your opinion is valuable to us. To help us improve, we'd like to ask you a few questions about your experience. It will only take 3 minutes and your answers will help us make Virtual Labs better for you and other users. </p>
<buttonclass="btn btn-default btn-lg" id="feedback-btn" type="button">Share Your Experience</button></a>
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<p> Thanks for your time !
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<br/><b>The Virtual Labs Team</b></p></div>
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<p><b>Dear User</b>,</p>
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<p>Thanks for using Virtual Labs. Your opinion is valuable to us. To help us improve, we'd like to ask you a few questions about your experience. It will only take 3 minutes and your answers will help us make Virtual Labs better for you and other users.
<!--p>Before you start any of the experiments, make sure you read the <a href="#notes">notes</a> section below. </p>
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<h2> <a name="notes">Notes on Experiments</a> </h2-->
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<p> The experiments are designed to expose you to certain concepts. You are encouraged to carry out variants of the suggested experiments and learn from them. Please make sure that you read the background material and experiment procedure before carrying out the experiments. Trying out the experiments before this does not hurt, but having the background will help you understand the results better. </p>
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<p><b>Please note:</b></p>
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<p> - This lab requires few Software Dependencies to run the Simulations </p>
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<ol>
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<li> Java1.7,</li>
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<li> Browsers Firefox 50 and it's below versions, internet explorer.</li>
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<li> IcedTea Plugin for Linux Operating System.</li>
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</ol>
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<p> - We recommend the following for the smooth running of these simulations</p>
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<ol>
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<li> Install VirtualBox - Virtual Labs offers a free download of a customized VirtualBox with the required pre installed all the software dependencies(Java 1.7). Please follow the <ahref="https://docs.google.com/document/d/1mX0YMtFk4Dxn_vQBJmUJ8pIOPE2hVf6AscJMdLWd9k8/edit#">link</a> to download, install and use VirtualBox for Virtual Labs.</li>
<li><ahref="exp6/Introduction.html?domain=Computer Science&lab=Pattern Recognition Lab"><strong> MLE: Learning the classifier from data </strong></a></li>
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<li><ahref="exp7/Introduction.html?domain=Computer Science&lab=Pattern Recognition Lab"><strong> Data Clustering: K-Means, MST-based </strong></a></li>
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