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updated schedule with video

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Morten Hjorth-Jensen
2022-09-10 21:33:46 +02:00
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>7. Optimization, the central part of any Machine Learning algortithm &#8212; Applied Data Analysis and Machine Learning</title>
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<script>var togglebuttonSelector = '.toggle, .admonition.dropdown, .tag_hide_input div.cell_input, .tag_hide-input div.cell_input, .tag_hide_output div.cell_output, .tag_hide-output div.cell_output, .tag_hide_cell.cell, .tag_hide-cell.cell';</script>
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</a>
</li>
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<p aria-level="2" class="caption" role="heading">
<p class="caption" role="heading">
<span class="caption-text">
About the course
</span>
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</a>
</li>
</ul>
<p aria-level="2" class="caption" role="heading">
<p class="caption" role="heading">
<span class="caption-text">
Review of Statistics with Resampling Techniques and Linear Algebra
</span>
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</a>
</li>
</ul>
<p aria-level="2" class="caption" role="heading">
<p class="caption" role="heading">
<span class="caption-text">
From Regression to Support Vector Machines
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</a>
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<p aria-level="2" class="caption" role="heading">
<p class="caption" role="heading">
<span class="caption-text">
Decision Trees, Ensemble Methods and Boosting
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Dimensionality Reduction
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Deep Learning Methods
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<h1>Optimization, the central part of any Machine Learning algortithm</h1>
<!-- Table of contents -->
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<h2> Contents </h2>
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<ul class="visible nav section-nav flex-column">
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#steepest-descent">
7.1. Steepest descent
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<a class="reference internal nav-link" href="#convex-functions">
7.2. Convex functions
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<a class="reference internal nav-link" href="#some-simple-problems">
7.2.1. Some simple problems
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<a class="reference internal nav-link" href="#standard-steepest-descent">
7.3. Standard steepest descent
</a>
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<a class="reference internal nav-link" href="#conjugate-gradient-method">
7.4. Conjugate gradient method
</a>
</li>
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#revisiting-our-linear-regression-solvers">
7.5. Revisiting our Linear Regression Solvers
</a>
</li>
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#using-gradient-descent-methods-limitations">
7.6. Using gradient descent methods, limitations
</a>
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<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#stochastic-gradient-descent-sgd">
7.7. Stochastic Gradient Descent (SGD)
</a>
<ul class="nav section-nav flex-column">
<li class="toc-h3 nav-item toc-entry">
<a class="reference internal nav-link" href="#program-for-stochastic-gradient">
7.7.1. Program for stochastic gradient
</a>
</li>
</ul>
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<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#momentum-based-gd">
7.8. Momentum based GD
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<ul class="nav section-nav flex-column">
<li class="toc-h3 nav-item toc-entry">
<a class="reference internal nav-link" href="#rms-prop">
7.8.1. RMS prop
</a>
</li>
<li class="toc-h3 nav-item toc-entry">
<a class="reference internal nav-link" href="#adam-optimizer">
7.8.2. ADAM optimizer
</a>
</li>
</ul>
</li>
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#practical-tips">
7.9. Practical tips
</a>
</li>
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#automatic-differentiation">
7.10. Automatic differentiation
</a>
</li>
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<a class="reference internal nav-link" href="#using-autograd-with-ols">
7.11. Using Autograd with OLS
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<a class="reference internal nav-link" href="#including-stochastic-gradient-descent-with-autograd">
7.11.1. Including Stochastic Gradient Descent with Autograd
</a>
</li>
<li class="toc-h3 nav-item toc-entry">
<a class="reference internal nav-link" href="#and-logistic-regression">
7.11.2. And Logistic Regression
</a>
</li>
</ul>
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@@ -2509,42 +2395,54 @@ first example shows results with ordinary leats squares.</p>
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