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<title>Machine Learning</title>
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<h1 class="title">Machine Learning</h1>
<address class="author">Mort Yao</address>
<!-- h3 class="date">2017-01-19</h3 -->
</header>
<nav id="TOC">
<ul>
<li><a href="#learning-theory"><span class="toc-section-number">1</span> Learning Theory</a></li>
<li><a href="#supervised-learning"><span class="toc-section-number">2</span> Supervised Learning</a></li>
<li><a href="#unsupervised-learning"><span class="toc-section-number">3</span> Unsupervised Learning</a></li>
<li><a href="#online-learning-models"><span class="toc-section-number">4</span> Online Learning Models</a></li>
<li><a href="#decomposition-and-dimensionality-reduction-methods"><span class="toc-section-number">5</span> Decomposition and Dimensionality Reduction Methods</a></li>
<li><a href="#anomaly-detection-methods"><span class="toc-section-number">6</span> Anomaly Detection Methods</a></li>
<li><a href="#structured-prediction-methods"><span class="toc-section-number">7</span> Structured Prediction Methods</a></li>
<li><a href="#neural-networks-and-deep-learning"><span class="toc-section-number">8</span> Neural Networks and Deep Learning</a></li>
<li><a href="#reinforcement-learning"><span class="toc-section-number">9</span> Reinforcement Learning</a></li>
</ul>
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<div id="content">
<p>Reading:</p>
<ul>
<li>Christopher Bishop. <strong><em>Pattern Recognition and Machine Learning.</em></strong> (<strong>PRML</strong>)</li>
<li>Yaser S. Abu-Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin. <strong><em>Learning from Data: A Short Course.</em></strong></li>
<li>Yevgeny Seldin. <strong><em>Machine Learning Lecture Notes.</em></strong></li>
<li>Su-Yun Huang, Kuang-Yao Lee and Horng-Shing Lu. <a href="http://www.stat.sinica.edu.tw/syhuang/kern_stat_toolbox/lecture-notes.pdf"><strong><em>Lecture Notes: Statistical and Machine Learning.</em></strong></a></li>
</ul>
<hr />
<section id="learning-theory" class="level1">
<h1><span class="header-section-number">1</span> Learning Theory</h1>
<p>Preliminaries: Basic <a href="/math/probability/">probability theory</a>, <a href="/math/statistics/">statistics</a>, <a href="/info/">information theory</a>, <a href="/comp/">theory of computation</a> (NP-hard problems).</p>
<section id="generalization-bounds" class="level2">
<h2><span class="header-section-number">1.1</span> Generalization Bounds</h2>
</section>
<section id="occam-learning" class="level2">
<h2><span class="header-section-number">1.2</span> Occam Learning</h2>
</section>
<section id="pac-learning" class="level2">
<h2><span class="header-section-number">1.3</span> PAC Learning</h2>
</section>
<section id="vapnik-chervonenkis-vc-theory" class="level2">
<h2><span class="header-section-number">1.4</span> Vapnik-Chervonenkis (VC) Theory</h2>
</section>
</section>
<section id="supervised-learning" class="level1">
<h1><span class="header-section-number">2</span> Supervised Learning</h1>
<section id="classification" class="level2">
<h2><span class="header-section-number">2.1</span> Classification</h2>
<section id="perceptron" class="level3">
<h3><span class="header-section-number">2.1.1</span> Perceptron</h3>
</section>
<section id="naive-bayes-classifier" class="level3">
<h3><span class="header-section-number">2.1.2</span> Naive Bayes Classifier</h3>
</section>
</section>
<section id="regression" class="level2">
<h2><span class="header-section-number">2.2</span> Regression</h2>
<section id="simple-linear-regression-linear-least-squares" class="level3">
<h3><span class="header-section-number">2.2.1</span> Simple Linear Regression: Linear Least Squares</h3>
</section>
<section id="bayesian-linear-regression" class="level3">
<h3><span class="header-section-number">2.2.2</span> Bayesian Linear Regression</h3>
</section>
<section id="logistic-regression" class="level3">
<h3><span class="header-section-number">2.2.3</span> Logistic Regression</h3>
</section>
<section id="generalized-linear-model-glm-and-lasso" class="level3">
<h3><span class="header-section-number">2.2.4</span> Generalized Linear Model (GLM) and LASSO</h3>
</section>
</section>
<section id="k-nearest-neighbors-k-nn" class="level2">
<h2><span class="header-section-number">2.3</span> k-Nearest Neighbors (<span class="math inline">\(k\)</span>-NN)</h2>
</section>
<section id="kernel-methods" class="level2">
<h2><span class="header-section-number">2.4</span> Kernel Methods</h2>
<section id="support-vector-machine-svm" class="level3">
<h3><span class="header-section-number">2.4.1</span> Support Vector Machine (SVM)</h3>
</section>
</section>
<section id="decision-trees-and-ensembles" class="level2">
<h2><span class="header-section-number">2.5</span> Decision Trees and Ensembles</h2>
<section id="bagging" class="level3">
<h3><span class="header-section-number">2.5.1</span> Bagging</h3>
</section>
<section id="boosting" class="level3">
<h3><span class="header-section-number">2.5.2</span> Boosting</h3>
</section>
<section id="random-forests" class="level3">
<h3><span class="header-section-number">2.5.3</span> Random Forests</h3>
</section>
</section>
</section>
<section id="unsupervised-learning" class="level1">
<h1><span class="header-section-number">3</span> Unsupervised Learning</h1>
<section id="clustering" class="level2">
<h2><span class="header-section-number">3.1</span> Clustering</h2>
<section id="k-means-and-k-means" class="level3">
<h3><span class="header-section-number">3.1.1</span> <span class="math inline">\(k\)</span>-means and <span class="math inline">\(k\)</span>-means++</h3>
</section>
<section id="mean-shift" class="level3">
<h3><span class="header-section-number">3.1.2</span> Mean Shift</h3>
</section>
<section id="expectationmaximization-em" class="level3">
<h3><span class="header-section-number">3.1.3</span> Expectation–Maximization (EM)</h3>
</section>
</section>
<section id="density-estimation" class="level2">
<h2><span class="header-section-number">3.2</span> Density Estimation</h2>
<section id="kernel-density-estimation-kde" class="level3">
<h3><span class="header-section-number">3.2.1</span> Kernel Density Estimation (KDE)</h3>
</section>
</section>
</section>
<section id="online-learning-models" class="level1">
<h1><span class="header-section-number">4</span> Online Learning Models</h1>
<section id="statistical-learning-model" class="level2">
<h2><span class="header-section-number">4.1</span> Statistical Learning Model</h2>
</section>
<section id="adversarial-model" class="level2">
<h2><span class="header-section-number">4.2</span> Adversarial Model</h2>
</section>
</section>
<section id="decomposition-and-dimensionality-reduction-methods" class="level1">
<h1><span class="header-section-number">5</span> Decomposition and Dimensionality Reduction Methods</h1>
<section id="singular-value-decomposition-svd" class="level2">
<h2><span class="header-section-number">5.1</span> Singular Value Decomposition (SVD)</h2>
</section>
<section id="principal-component-analysis-pca" class="level2">
<h2><span class="header-section-number">5.2</span> Principal Component Analysis (PCA)</h2>
</section>
<section id="factor-analysis" class="level2">
<h2><span class="header-section-number">5.3</span> Factor Analysis</h2>
</section>
<section id="independent-component-analysis-ica" class="level2">
<h2><span class="header-section-number">5.4</span> Independent Component Analysis (ICA)</h2>
</section>
<section id="nonlinear-dimensionality-reduction-nldr-and-manifold-learning" class="level2">
<h2><span class="header-section-number">5.5</span> Nonlinear Dimensionality Reduction (NLDR) and Manifold Learning</h2>
</section>
</section>
<section id="anomaly-detection-methods" class="level1">
<h1><span class="header-section-number">6</span> Anomaly Detection Methods</h1>
</section>
<section id="structured-prediction-methods" class="level1">
<h1><span class="header-section-number">7</span> Structured Prediction Methods</h1>
<section id="bayesian-network" class="level2">
<h2><span class="header-section-number">7.1</span> Bayesian Network</h2>
</section>
<section id="hidden-markov-model-hmm" class="level2">
<h2><span class="header-section-number">7.2</span> Hidden Markov Model (HMM)</h2>
</section>
<section id="conditional-random-field-crf" class="level2">
<h2><span class="header-section-number">7.3</span> Conditional Random Field (CRF)</h2>
</section>
<section id="hierarchical-temporal-memory-htm" class="level2">
<h2><span class="header-section-number">7.4</span> Hierarchical Temporal Memory (HTM)</h2>
</section>
</section>
<section id="neural-networks-and-deep-learning" class="level1">
<h1><span class="header-section-number">8</span> Neural Networks and Deep Learning</h1>
</section>
<section id="reinforcement-learning" class="level1">
<h1><span class="header-section-number">9</span> Reinforcement Learning</h1>
</section>
</div>
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