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mhjensen committed Dec 21, 2023
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33 changes: 20 additions & 13 deletions doc/pub/week1/html/week1-bs.html
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<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="DocOnce: https://github.com/doconce/doconce/" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<meta name="description" content="January 23-27: Advanced machine learning and data analysis for the physical sciences">
<title>January 23-27: Advanced machine learning and data analysis for the physical sciences</title>
<meta name="description" content="January 15-19: Advanced machine learning and data analysis for the physical sciences">
<title>January 15-19: Advanced machine learning and data analysis for the physical sciences</title>
<!-- Bootstrap style: bootstrap -->
<!-- doconce format html week1.do.txt --html_style=bootstrap --pygments_html_style=default --html_admon=bootstrap_panel --html_output=week1-bs --no_mako -->
<link href="https://netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css" rel="stylesheet">
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<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<a class="navbar-brand" href="week1-bs.html">January 23-27: Advanced machine learning and data analysis for the physical sciences</a>
<a class="navbar-brand" href="week1-bs.html">January 15-19: Advanced machine learning and data analysis for the physical sciences</a>
</div>
<div class="navbar-collapse collapse navbar-responsive-collapse">
<ul class="nav navbar-nav navbar-right">
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<!-- ------------------- main content ---------------------- -->
<div class="jumbotron">
<center>
<h1>January 23-27: Advanced machine learning and data analysis for the physical sciences</h1>
<h1>January 15-19: Advanced machine learning and data analysis for the physical sciences</h1>
</center> <!-- document title -->

<!-- author(s): Morten Hjorth-Jensen -->
Expand All @@ -136,7 +136,7 @@ <h1>January 23-27: Advanced machine learning and data analysis for the physical
</center>
<br>
<center>
<h4>Jan 25, 2023</h4>
<h4>Dec 21, 2023</h4>
</center> <!-- date -->
<br>

Expand All @@ -159,8 +159,6 @@ <h2 id="overview-of-week-first-week" class="anchor">Overview of week first week
</div>


<a href="https://youtube" target="_self">Video of lecture</a>

<!-- !split -->
<h2 id="practicalities-and-possible-projects" class="anchor">Practicalities and possible projects </h2>

Expand All @@ -176,18 +174,27 @@ <h2 id="practicalities-and-possible-projects" class="anchor">Practicalities and
<li> Bayesian Machine Learning and Gaussian processes</li>
<li> and many other research paths and topics</li>
</ul>
<li> Final oral examination to be agreed upon</li>
<li> No exam, only two projects whoch count 1/2 of the final grade each</li>
<li> All info at the GitHub address <a href="https://github.com/CompPhysics/AdvancedMachineLearning" target="_self"><tt>https://github.com/CompPhysics/AdvancedMachineLearning</tt></a></li>
</ol>
<!-- !split -->
<h2 id="deep-learning-methods-covered" class="anchor">Deep learning methods covered </h2>

<ol>
<li> Feed forward neural networks (NNs)</li>
<li> Convolutional neural networks (CNNs)</li>
<li> Recurrent neural networks (RNNs)</li>
<li> Autoencoders (AEs) and variational autoencoders (VAEe)</li>
<li> Generative Adversarial Networks (GANs)</li>
<li> Deep learning, classics
<ol type="a"></li>
<li> Feed forward neural networks and its mathematics (NNs)</li>
<li> Convolutional neural networks (CNNs)</li>
<li> Recurrent neural networks (RNNs)</li>
<li> Autoencoders and principal component analysis</li>
<li> Physics informed neural networks</li>
</ol>
<li> Deeep learning, generative methods
<ol type="a"></li>
<li> Boltzmann machines and energy based methods</li>
<li> Autoencodervariational autoencoders (VAEe)</li>
<li> Generative Adversarial Networks (GANs)</li>
</ol>
</ol>
<p>The <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html" target="_self">lecture notes</a> contain a more in depth discussion of these methods, in particular on neural networks, CNNs and RNNs.</p>

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34 changes: 21 additions & 13 deletions doc/pub/week1/html/week1-reveal.html
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<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="DocOnce: https://github.com/doconce/doconce/" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<meta name="description" content="January 23-27: Advanced machine learning and data analysis for the physical sciences">
<title>January 23-27: Advanced machine learning and data analysis for the physical sciences</title>
<meta name="description" content="January 15-19: Advanced machine learning and data analysis for the physical sciences">
<title>January 15-19: Advanced machine learning and data analysis for the physical sciences</title>

<!-- reveal.js: https://lab.hakim.se/reveal-js/ -->

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<section>
<!-- ------------------- main content ---------------------- -->
<center>
<h1 style="text-align: center;">January 23-27: Advanced machine learning and data analysis for the physical sciences</h1>
<h1 style="text-align: center;">January 15-19: Advanced machine learning and data analysis for the physical sciences</h1>
</center> <!-- document title -->

<!-- author(s): Morten Hjorth-Jensen -->
Expand All @@ -184,7 +184,7 @@ <h1 style="text-align: center;">January 23-27: Advanced machine learning and dat
</center>
<br>
<center>
<h4>Jan 25, 2023</h4>
<h4>Dec 21, 2023</h4>
</center> <!-- date -->
<br>

Expand All @@ -207,9 +207,6 @@ <h2 id="overview-of-week-first-week">Overview of week first week </h2>
<p><li> Eventual start with theory discussions on deep learning methods</li>
</ol>
</div>


<a href="https://youtube" target="_blank">Video of lecture</a>
</section>

<section>
Expand All @@ -228,7 +225,7 @@ <h2 id="practicalities-and-possible-projects">Practicalities and possible projec
<p><li> and many other research paths and topics</li>
</ul>
<p>
<p><li> Final oral examination to be agreed upon</li>
<p><li> No exam, only two projects whoch count 1/2 of the final grade each</li>
<p><li> All info at the GitHub address <a href="https://github.com/CompPhysics/AdvancedMachineLearning" target="_blank"><tt>https://github.com/CompPhysics/AdvancedMachineLearning</tt></a></li>
</ol>
</section>
Expand All @@ -237,11 +234,22 @@ <h2 id="practicalities-and-possible-projects">Practicalities and possible projec
<h2 id="deep-learning-methods-covered">Deep learning methods covered </h2>

<ol>
<p><li> Feed forward neural networks (NNs)</li>
<p><li> Convolutional neural networks (CNNs)</li>
<p><li> Recurrent neural networks (RNNs)</li>
<p><li> Autoencoders (AEs) and variational autoencoders (VAEe)</li>
<p><li> Generative Adversarial Networks (GANs)</li>
<p><li> Deep learning, classics
<ol type="a"></li>
<p><li> Feed forward neural networks and its mathematics (NNs)</li>
<p><li> Convolutional neural networks (CNNs)</li>
<p><li> Recurrent neural networks (RNNs)</li>
<p><li> Autoencoders and principal component analysis</li>
<p><li> Physics informed neural networks</li>
</ol>
<p>
<p><li> Deeep learning, generative methods
<ol type="a"></li>
<p><li> Boltzmann machines and energy based methods</li>
<p><li> Autoencodervariational autoencoders (VAEe)</li>
<p><li> Generative Adversarial Networks (GANs)</li>
</ol>
<p>
</ol>
<p>
<p>The <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html" target="_blank">lecture notes</a> contain a more in depth discussion of these methods, in particular on neural networks, CNNs and RNNs.</p>
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31 changes: 19 additions & 12 deletions doc/pub/week1/html/week1-solarized.html
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<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="DocOnce: https://github.com/doconce/doconce/" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<meta name="description" content="January 23-27: Advanced machine learning and data analysis for the physical sciences">
<title>January 23-27: Advanced machine learning and data analysis for the physical sciences</title>
<meta name="description" content="January 15-19: Advanced machine learning and data analysis for the physical sciences">
<title>January 15-19: Advanced machine learning and data analysis for the physical sciences</title>
<link href="https://cdn.rawgit.com/doconce/doconce/master/bundled/html_styles/style_solarized_box/css/solarized_light_code.css" rel="stylesheet" type="text/css" title="light"/>
<script src="https://cdn.rawgit.com/doconce/doconce/master/bundled/html_styles/style_solarized_box/js/highlight.pack.js"></script>
<script>hljs.initHighlightingOnLoad();</script>
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<!-- ------------------- main content ---------------------- -->
<center>
<h1>January 23-27: Advanced machine learning and data analysis for the physical sciences</h1>
<h1>January 15-19: Advanced machine learning and data analysis for the physical sciences</h1>
</center> <!-- document title -->

<!-- author(s): Morten Hjorth-Jensen -->
Expand All @@ -131,7 +131,7 @@ <h1>January 23-27: Advanced machine learning and data analysis for the physical
</center>
<br>
<center>
<h4>Jan 25, 2023</h4>
<h4>Dec 21, 2023</h4>
</center> <!-- date -->
<br>

Expand All @@ -150,8 +150,6 @@ <h2 id="overview-of-week-first-week">Overview of week first week </h2>
</div>


<a href="https://youtube" target="_blank">Video of lecture</a>

<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="practicalities-and-possible-projects">Practicalities and possible projects </h2>

Expand All @@ -167,18 +165,27 @@ <h2 id="practicalities-and-possible-projects">Practicalities and possible projec
<li> Bayesian Machine Learning and Gaussian processes</li>
<li> and many other research paths and topics</li>
</ul>
<li> Final oral examination to be agreed upon</li>
<li> No exam, only two projects whoch count 1/2 of the final grade each</li>
<li> All info at the GitHub address <a href="https://github.com/CompPhysics/AdvancedMachineLearning" target="_blank"><tt>https://github.com/CompPhysics/AdvancedMachineLearning</tt></a></li>
</ol>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="deep-learning-methods-covered">Deep learning methods covered </h2>

<ol>
<li> Feed forward neural networks (NNs)</li>
<li> Convolutional neural networks (CNNs)</li>
<li> Recurrent neural networks (RNNs)</li>
<li> Autoencoders (AEs) and variational autoencoders (VAEe)</li>
<li> Generative Adversarial Networks (GANs)</li>
<li> Deep learning, classics
<ol type="a"></li>
<li> Feed forward neural networks and its mathematics (NNs)</li>
<li> Convolutional neural networks (CNNs)</li>
<li> Recurrent neural networks (RNNs)</li>
<li> Autoencoders and principal component analysis</li>
<li> Physics informed neural networks</li>
</ol>
<li> Deeep learning, generative methods
<ol type="a"></li>
<li> Boltzmann machines and energy based methods</li>
<li> Autoencodervariational autoencoders (VAEe)</li>
<li> Generative Adversarial Networks (GANs)</li>
</ol>
</ol>
<p>The <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html" target="_blank">lecture notes</a> contain a more in depth discussion of these methods, in particular on neural networks, CNNs and RNNs.</p>

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31 changes: 19 additions & 12 deletions doc/pub/week1/html/week1.html
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<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="DocOnce: https://github.com/doconce/doconce/" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<meta name="description" content="January 23-27: Advanced machine learning and data analysis for the physical sciences">
<title>January 23-27: Advanced machine learning and data analysis for the physical sciences</title>
<meta name="description" content="January 15-19: Advanced machine learning and data analysis for the physical sciences">
<title>January 15-19: Advanced machine learning and data analysis for the physical sciences</title>
<style type="text/css">
/* bloodish style */
body {
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<!-- ------------------- main content ---------------------- -->
<center>
<h1>January 23-27: Advanced machine learning and data analysis for the physical sciences</h1>
<h1>January 15-19: Advanced machine learning and data analysis for the physical sciences</h1>
</center> <!-- document title -->

<!-- author(s): Morten Hjorth-Jensen -->
Expand All @@ -208,7 +208,7 @@ <h1>January 23-27: Advanced machine learning and data analysis for the physical
</center>
<br>
<center>
<h4>Jan 25, 2023</h4>
<h4>Dec 21, 2023</h4>
</center> <!-- date -->
<br>

Expand All @@ -227,8 +227,6 @@ <h2 id="overview-of-week-first-week">Overview of week first week </h2>
</div>


<a href="https://youtube" target="_blank">Video of lecture</a>

<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="practicalities-and-possible-projects">Practicalities and possible projects </h2>

Expand All @@ -244,18 +242,27 @@ <h2 id="practicalities-and-possible-projects">Practicalities and possible projec
<li> Bayesian Machine Learning and Gaussian processes</li>
<li> and many other research paths and topics</li>
</ul>
<li> Final oral examination to be agreed upon</li>
<li> No exam, only two projects whoch count 1/2 of the final grade each</li>
<li> All info at the GitHub address <a href="https://github.com/CompPhysics/AdvancedMachineLearning" target="_blank"><tt>https://github.com/CompPhysics/AdvancedMachineLearning</tt></a></li>
</ol>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="deep-learning-methods-covered">Deep learning methods covered </h2>

<ol>
<li> Feed forward neural networks (NNs)</li>
<li> Convolutional neural networks (CNNs)</li>
<li> Recurrent neural networks (RNNs)</li>
<li> Autoencoders (AEs) and variational autoencoders (VAEe)</li>
<li> Generative Adversarial Networks (GANs)</li>
<li> Deep learning, classics
<ol type="a"></li>
<li> Feed forward neural networks and its mathematics (NNs)</li>
<li> Convolutional neural networks (CNNs)</li>
<li> Recurrent neural networks (RNNs)</li>
<li> Autoencoders and principal component analysis</li>
<li> Physics informed neural networks</li>
</ol>
<li> Deeep learning, generative methods
<ol type="a"></li>
<li> Boltzmann machines and energy based methods</li>
<li> Autoencodervariational autoencoders (VAEe)</li>
<li> Generative Adversarial Networks (GANs)</li>
</ol>
</ol>
<p>The <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html" target="_blank">lecture notes</a> contain a more in depth discussion of these methods, in particular on neural networks, CNNs and RNNs.</p>

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