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<title>Working with Big Data: Infrastructure, Algorithms, and Visualizations</title>
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<h1>Lesson 4: Working with Algorithms</h1>
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<section class="slide">
<h1>K-Nearest Neighbors</h1>
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<section class="slide">
<h2>Algorithms in Big Data</h2>
<ol>
<li class="slide"><h3>Machine Learning</h3></li>
<li class="slide"><h3>Efficiency, e.g., Bloom Filters</h3></li>
<li class="slide"><h3>Distributed Computing, e.g, Paxos</h3></li>
</ol>
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<h2>Machine Learning</h2>
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<li class="slide"><h3>Classification or Supervised Learning</h3></li>
<li class="slide"><h3>Clustering or Unsupervised Learning</h3></li>
<li class="slide"><h3>Semi-supervised Learning</h3></li>
<li class="slide"><h3>Dimensionality Reduction</h3></li>
<li class="slide"><h3>Collaborative Filtering</h3></li>
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<h2>Machine Learning is Good For</h2>
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<li class="slide"><h3>Spam Detection</h3></li>
<li class="slide"><h3>Topic Assignment</h3></li>
<li class="slide"><h3>Sentiment Detection</h3></li>
<li class="slide"><h3>Product, Music, or Content Recommendations</h3></li>
<li class="slide"><h3>Ranking Search Results</h3></li>
<li class="slide"><h3>Ad Targeting</h3></li>
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<h2>Classification Workflow</h2>
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<li class="slide"><h3>Given labeled training data, train a model</h3></li>
<li class="slide"><h3>Test the model on hold out data</h3></li>
<li class="slide"><h3>Update the model based on testing</h3></li>
<li class="slide"><h3>Given a new instance, make a prediction</h3></li>
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<h2>Clustering Workflow</h2>
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<li class="slide"><h3>Given unlabeled training data, find clusters</h3></li>
<li class="slide"><h3>Examine the clusters for "approximate correctness"</h3></li>
<li class="slide"><h3>Update the representation or the algorithm and repeat</h3></li>
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<section class="slide">
<h1>On to implementing KNN...</h1>
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