mhjensen
246 lines
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HTML
246 lines
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<h1 class="site-logo" id="site-title">Applied Machine Learning and Data Analysis</h1>
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<a class="reference internal" href="chapter1.html">
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Introduction to Applied Data Analysis and Machine Learning
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</a>
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Supervised Learning
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<a class="reference internal" href="chapter2.html">
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1. Elements of Probability Theory and Statistical Data Analysis
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</a>
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<a class="reference internal" href="chapter3.html">
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2. Getting started, our first data and Machine Learning encounters
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</a>
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3. Linear Regression and more Advanced Regression Analysis
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</a>
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<a class="reference internal" href="chapter5.html">
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4. Logistic Regression
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</a>
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5. Neural networks, from the simple perceptron to deep learning
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</a>
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6. Support Vector Machines, overarching aims
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</a>
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<li class="toctree-l1">
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<a class="reference internal" href="chapter8.html">
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7. Dimensionality Reduction
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</a>
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<a class="reference internal" href="chapter9.html">
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8. Convolutional Neural Networks
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<a class="reference internal" href="chapter10.html">
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9. Recurrent Neural Networks
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<a class="reference internal" href="chapter10.html#solving-odes-with-deep-learning">
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10. Solving ODEs with Deep Learning
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<a class="reference internal" href="chapter11.html">
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11. Data Analysis and Machine Learning:
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</a>
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<a class="reference internal" href="chapter11.html#elements-of-bayesian-theory-and-bayesian-neural-networks">
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12. Elements of Bayesian theory and Bayesian Neural Networks
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By Morten Hjorth-Jensen<br/>
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© Copyright 2020.<br/>
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