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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">15 Anomaly Detection</a></span><span class="md-toc-item md-toc-h2" data-ref="n3"><a class="md-toc-inner" href="#header-n3">15.1 Problem Motivation</a></span><span class="md-toc-item md-toc-h2" data-ref="n4"><a class="md-toc-inner" href="#header-n4">15.2 Gaussian Distribution</a></span><span class="md-toc-item md-toc-h2" data-ref="n5"><a class="md-toc-inner" href="#header-n5">15.3 Algorithm</a></span><span class="md-toc-item md-toc-h2" data-ref="n6"><a class="md-toc-inner" href="#header-n6">15.4 Developing and Evaluating an Anomaly Detection System</a></span><span class="md-toc-item md-toc-h2" data-ref="n7"><a class="md-toc-inner" href="#header-n7">15.5 Anomaly Detection vs. Supervised Learning</a></span><span class="md-toc-item md-toc-h2" data-ref="n8"><a class="md-toc-inner" href="#header-n8">15.6 Choosing What Features to Use</a></span><span class="md-toc-item md-toc-h2" data-ref="n9"><a class="md-toc-inner" href="#header-n9">15.7 Multivariate Gaussian Distribution</a></span><span class="md-toc-item md-toc-h2" data-ref="n10"><a class="md-toc-inner" href="#header-n10">15.8 Anomaly Detection using the Multivariate Gaussian Distribution</a></span><span class="md-toc-item md-toc-h1" data-ref="n11"><a class="md-toc-inner" href="#header-n11">16 Recommender Systems</a></span><span class="md-toc-item md-toc-h2" data-ref="n12"><a class="md-toc-inner" href="#header-n12">16.1 Problem Formulation</a></span><span class="md-toc-item md-toc-h2" data-ref="n13"><a class="md-toc-inner" href="#header-n13">16.2 Content Based Recommendations</a></span><span class="md-toc-item md-toc-h2" data-ref="n14"><a class="md-toc-inner" href="#header-n14">16.3 Collaborative Filtering</a></span><span class="md-toc-item md-toc-h2" data-ref="n15"><a class="md-toc-inner" href="#header-n15">16.4 Collaborative Filtering Algorithm</a></span><span class="md-toc-item md-toc-h2" data-ref="n16"><a class="md-toc-inner" href="#header-n16">16.5 Vectorization_ Low Rank Matrix Factorization</a></span><span class="md-toc-item md-toc-h2" data-ref="n17"><a class="md-toc-inner" href="#header-n17">16.6 Implementational Detail_ Mean Normalization</a></span></p></div><h1><a name='header-n2' class='md-header-anchor '></a>15 Anomaly Detection</h1><h2><a name='header-n3' class='md-header-anchor '></a>15.1 Problem Motivation</h2><h2><a name='header-n4' class='md-header-anchor '></a>15.2 Gaussian Distribution</h2><h2><a name='header-n5' class='md-header-anchor '></a>15.3 Algorithm</h2><h2><a name='header-n6' class='md-header-anchor '></a>15.4 Developing and Evaluating an Anomaly Detection System</h2><h2><a name='header-n7' class='md-header-anchor '></a>15.5 Anomaly Detection vs. Supervised Learning</h2><h2><a name='header-n8' class='md-header-anchor '></a>15.6 Choosing What Features to Use</h2><h2><a name='header-n9' class='md-header-anchor '></a>15.7 Multivariate Gaussian Distribution</h2><h2><a name='header-n10' class='md-header-anchor '></a>15.8 Anomaly Detection using the Multivariate Gaussian Distribution</h2><h1><a name='header-n11' class='md-header-anchor '></a>16 Recommender Systems</h1><h2><a name='header-n12' class='md-header-anchor '></a>16.1 Problem Formulation</h2><h2><a name='header-n13' class='md-header-anchor '></a>16.2 Content Based Recommendations</h2><h2><a name='header-n14' class='md-header-anchor '></a>16.3 Collaborative Filtering</h2><h2><a name='header-n15' class='md-header-anchor '></a>16.4 Collaborative Filtering Algorithm</h2><h2><a name='header-n16' class='md-header-anchor '></a>16.5 Vectorization_ Low Rank Matrix Factorization</h2><h2><a name='header-n17' class='md-header-anchor '></a>16.6 Implementational Detail_ Mean Normalization</h2></div>
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