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<div  id='write'  class = 'is-node show-fences-line-number'><div class='md-toc' mdtype='toc'><p class="md-toc-content"><span class="md-toc-item md-toc-h1" data-ref="n2"><a class="md-toc-inner" href="#header-n2">13 Unsupervised Learning</a></span><span class="md-toc-item md-toc-h2" data-ref="n3"><a class="md-toc-inner" href="#header-n3">13.1 Unsupervised Learning_ Introduction</a></span><span class="md-toc-item md-toc-h2" data-ref="n4"><a class="md-toc-inner" href="#header-n4">13.2 K-Means Algorithm</a></span><span class="md-toc-item md-toc-h2" data-ref="n5"><a class="md-toc-inner" href="#header-n5">13.3 Optimization Objective</a></span><span class="md-toc-item md-toc-h2" data-ref="n6"><a class="md-toc-inner" href="#header-n6">13.4 Random Initialization</a></span><span class="md-toc-item md-toc-h2" data-ref="n7"><a class="md-toc-inner" href="#header-n7">13.5 Choosing the Number of Clusters</a></span><span class="md-toc-item md-toc-h1" data-ref="n8"><a class="md-toc-inner" href="#header-n8">14 Dimensionality Reduction</a></span><span class="md-toc-item md-toc-h2" data-ref="n9"><a class="md-toc-inner" href="#header-n9">14.1 Motivation I_ Data Compression</a></span><span class="md-toc-item md-toc-h2" data-ref="n10"><a class="md-toc-inner" href="#header-n10">14.2 Motivation II_ Visualization</a></span><span class="md-toc-item md-toc-h2" data-ref="n11"><a class="md-toc-inner" href="#header-n11">14.3 Principal Component Analysis Problem Formulation</a></span><span class="md-toc-item md-toc-h2" data-ref="n12"><a class="md-toc-inner" href="#header-n12">14.4 Principal Component Analysis Algorithm</a></span><span class="md-toc-item md-toc-h2" data-ref="n13"><a class="md-toc-inner" href="#header-n13">14.5 Reconstruction from Compressed Representation</a></span><span class="md-toc-item md-toc-h2" data-ref="n14"><a class="md-toc-inner" href="#header-n14">14.6 Choosing the Number of Principal Components</a></span><span class="md-toc-item md-toc-h2" data-ref="n15"><a class="md-toc-inner" href="#header-n15">14.7 Advice for Applying PCA</a></span></p></div><h1><a name='header-n2' class='md-header-anchor '></a>13 Unsupervised Learning</h1><h2><a name='header-n3' class='md-header-anchor '></a>13.1 Unsupervised Learning_ Introduction</h2><h2><a name='header-n4' class='md-header-anchor '></a>13.2 K-Means Algorithm</h2><h2><a name='header-n5' class='md-header-anchor '></a>13.3 Optimization Objective</h2><h2><a name='header-n6' class='md-header-anchor '></a>13.4 Random Initialization</h2><h2><a name='header-n7' class='md-header-anchor '></a>13.5 Choosing the Number of Clusters</h2><h1><a name='header-n8' class='md-header-anchor '></a>14 Dimensionality Reduction</h1><h2><a name='header-n9' class='md-header-anchor '></a>14.1 Motivation I_ Data Compression</h2><h2><a name='header-n10' class='md-header-anchor '></a>14.2 Motivation II_ Visualization</h2><h2><a name='header-n11' class='md-header-anchor '></a>14.3 Principal Component Analysis Problem Formulation</h2><h2><a name='header-n12' class='md-header-anchor '></a>14.4 Principal Component Analysis Algorithm</h2><h2><a name='header-n13' class='md-header-anchor '></a>14.5 Reconstruction from Compressed Representation</h2><h2><a name='header-n14' class='md-header-anchor '></a>14.6 Choosing the Number of Principal Components</h2><h2><a name='header-n15' class='md-header-anchor '></a>14.7 Advice for Applying PCA</h2></div>
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