update first week

Author: mhjensen

doc/pub/week1/html/week1-bs.html

@@ -46,6 +46,7 @@
                None,
                'deep-learning-methods-covered-tentative'),
               ('Generative methods', 2, None, 'generative-methods'),
+              ('Reinforcement learning', 2, None, 'reinforcement-learning'),
               ('Both discriminative and generative',
                2,
                None,
@@ -364,6 +365,7 @@
      <!-- navigation toc: --> <li><a href="#practicalities" style="font-size: 80%;">Practicalities</a></li>
      <!-- navigation toc: --> <li><a href="#deep-learning-methods-covered-tentative" style="font-size: 80%;">Deep learning methods covered, tentative</a></li>
      <!-- navigation toc: --> <li><a href="#generative-methods" style="font-size: 80%;">Generative methods</a></li>
+     <!-- navigation toc: --> <li><a href="#reinforcement-learning" style="font-size: 80%;">Reinforcement learning</a></li>
      <!-- navigation toc: --> <li><a href="#both-discriminative-and-generative" style="font-size: 80%;">Both discriminative and generative</a></li>
      <!-- navigation toc: --> <li><a href="#additional-topics-kernel-regression-gaussian-processes-and-bayesian-statistics-https-jenfb-github-io-bkmr-overview-html" style="font-size: 80%;">"Additional topics:  Kernel regression (Gaussian processes) and Bayesian statistics":"https://jenfb.github.io/bkmr/overview.html"</a></li>
      <!-- navigation toc: --> <li><a href="#project-paths-overarching-view" style="font-size: 80%;">Project paths, overarching view</a></li>
@@ -491,8 +493,8 @@ <h2 id="overview-of-first-week-january-20-24-2025" class="anchor">Overview of fi
 <ol>
  <li> Presentation of course</li>
  <li> Discussion of possible projects</li>
- <li> Deep learning methods, mathematics and  review of neural networks</li>
- <li> "Video of lecture at <a href="https://youtu.be/SY57dC46L9o" target="_self"><tt>https://youtu.be/SY57dC46L9o</tt></a></li>
+ <li> Deep learning methods, mathematics and  review of neural networks
+<!-- o "Video of lecture at <a href="https://youtu.be/SY57dC46L9o" target="_self"><tt>https://youtu.be/SY57dC46L9o</tt></a> --></li>
  <li> Recommended reading first three weeks: Raschka et al chapter 11 and Goodfellow et al chapters 6 and 7</li>
  <li> Permanent Zoom link for the whole semester is <a href="https://uio.zoom.us/my/mortenhj" target="_self"><tt>https://uio.zoom.us/my/mortenhj</tt></a></li>
 </ol>
@@ -504,8 +506,8 @@ <h2 id="overview-of-first-week-january-20-24-2025" class="anchor">Overview of fi
 <h2 id="practicalities" class="anchor">Practicalities  </h2>
 
 <ol>
-<li> Lectures Thursdays 1215pm-2pm, room F&#216;434, Department of Physics</li>
-<li> Lab and exercise sessions Thursdays 215pm-4pm, room F&#216;434, Department of Physics</li> 
+<li> Lectures Thursdays 1015am-12pm, room F&#216;434, Department of Physics</li>
+<li> Lab and exercise sessions Thursdays 1215pm-2pm, room F&#216;434, Department of Physics</li> 
 <li> We plan to work on two projects which will define the content of the course, the format can be agreed upon by the participants</li>
 <li> No exam, only two projects. Each projects counts 1/2 of the final grade. Alternatively, one long project which counts 100% of the final grade</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>
@@ -519,9 +521,8 @@ <h2 id="deep-learning-methods-covered-tentative" class="anchor">Deep learning me
  <li> Feed forward neural networks and its mathematics (NNs)</li>
  <li> Convolutional neural networks (CNNs)</li>
  <li> Recurrent neural networks (RNNs)</li>
- <li> Graph neural networks</li>
- <li> Transformers</li>  
  <li> Autoencoders and principal component analysis</li>
+ <li> Transformers  (tentative)</li>
 </ol>
 </ol>
 <!-- !split -->
@@ -538,6 +539,12 @@ <h2 id="generative-methods" class="anchor">Generative methods </h2>
 </ol>
 </ol>
 <!-- !split -->
+<h2 id="reinforcement-learning" class="anchor">Reinforcement learning </h2>
+
+<ol>
+<li> Basics of rerinforcement learning (tentative)</li>
+</ol>
+<!-- !split -->
 <h2 id="both-discriminative-and-generative" class="anchor">Both discriminative and generative </h2>
 
 <ol>
@@ -557,8 +564,6 @@ <h2 id="additional-topics-kernel-regression-gaussian-processes-and-bayesian-stat
 variable).
 </p>
 
-<p>We have not made plans for Reinforcement learning.</p>
-
 <!-- !split -->
 <h2 id="project-paths-overarching-view" class="anchor">Project paths, overarching view </h2>
 

doc/pub/week1/html/week1-reveal.html

@@ -200,8 +200,8 @@ <h2 id="overview-of-first-week-january-20-24-2025">Overview of first week, Janua
 <ol>
  <p><li> Presentation of course</li>
  <p><li> Discussion of possible projects</li>
- <p><li> Deep learning methods, mathematics and  review of neural networks</li>
- <p><li> "Video of lecture at <a href="https://youtu.be/SY57dC46L9o" target="_blank"><tt>https://youtu.be/SY57dC46L9o</tt></a></li>
+ <p><li> Deep learning methods, mathematics and  review of neural networks
+<!-- o "Video of lecture at <a href="https://youtu.be/SY57dC46L9o" target="_blank"><tt>https://youtu.be/SY57dC46L9o</tt></a> --></li>
  <p><li> Recommended reading first three weeks: Raschka et al chapter 11 and Goodfellow et al chapters 6 and 7</li>
  <p><li> Permanent Zoom link for the whole semester is <a href="https://uio.zoom.us/my/mortenhj" target="_blank"><tt>https://uio.zoom.us/my/mortenhj</tt></a></li>
 </ol>
@@ -212,8 +212,8 @@ <h2 id="overview-of-first-week-january-20-24-2025">Overview of first week, Janua
 <h2 id="practicalities">Practicalities  </h2>
 
 <ol>
-<p><li> Lectures Thursdays 1215pm-2pm, room F&#216;434, Department of Physics</li>
-<p><li> Lab and exercise sessions Thursdays 215pm-4pm, room F&#216;434, Department of Physics</li> 
+<p><li> Lectures Thursdays 1015am-12pm, room F&#216;434, Department of Physics</li>
+<p><li> Lab and exercise sessions Thursdays 1215pm-2pm, room F&#216;434, Department of Physics</li> 
 <p><li> We plan to work on two projects which will define the content of the course, the format can be agreed upon by the participants</li>
 <p><li> No exam, only two projects. Each projects counts 1/2 of the final grade. Alternatively, one long project which counts 100% of the final grade</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>
@@ -229,10 +229,8 @@ <h2 id="deep-learning-methods-covered-tentative">Deep learning methods covered,
  <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> Graph neural networks</li>
- <p><li> Transformers</li>
-
-<p><li> Autoencoders and principal component analysis</li>
+ <p><li> Autoencoders and principal component analysis</li>
+ <p><li> Transformers  (tentative)</li>
 </ol>
 <p>
 </ol>
@@ -254,6 +252,14 @@ <h2 id="generative-methods">Generative methods </h2>
 </ol>
 </section>
 
+<section>
+<h2 id="reinforcement-learning">Reinforcement learning </h2>
+
+<ol>
+<p><li> Basics of rerinforcement learning (tentative)</li>
+</ol>
+</section>
+
 <section>
 <h2 id="both-discriminative-and-generative">Both discriminative and generative </h2>
 
@@ -275,8 +281,6 @@ <h2 id="additional-topics-kernel-regression-gaussian-processes-and-bayesian-stat
 large number of variables and a particular outcome (dependent
 variable).
 </p>
-
-<p>We have not made plans for Reinforcement learning.</p>
 </section>
 
 <section>

doc/pub/week1/html/week1-solarized.html

@@ -73,6 +73,7 @@
                None,
                'deep-learning-methods-covered-tentative'),
               ('Generative methods', 2, None, 'generative-methods'),
+              ('Reinforcement learning', 2, None, 'reinforcement-learning'),
               ('Both discriminative and generative',
                2,
                None,
@@ -400,8 +401,8 @@ <h2 id="overview-of-first-week-january-20-24-2025">Overview of first week, Janua
 <ol>
  <li> Presentation of course</li>
  <li> Discussion of possible projects</li>
- <li> Deep learning methods, mathematics and  review of neural networks</li>
- <li> "Video of lecture at <a href="https://youtu.be/SY57dC46L9o" target="_blank"><tt>https://youtu.be/SY57dC46L9o</tt></a></li>
+ <li> Deep learning methods, mathematics and  review of neural networks
+<!-- o "Video of lecture at <a href="https://youtu.be/SY57dC46L9o" target="_blank"><tt>https://youtu.be/SY57dC46L9o</tt></a> --></li>
  <li> Recommended reading first three weeks: Raschka et al chapter 11 and Goodfellow et al chapters 6 and 7</li>
  <li> Permanent Zoom link for the whole semester is <a href="https://uio.zoom.us/my/mortenhj" target="_blank"><tt>https://uio.zoom.us/my/mortenhj</tt></a></li>
 </ol>
@@ -412,8 +413,8 @@ <h2 id="overview-of-first-week-january-20-24-2025">Overview of first week, Janua
 <h2 id="practicalities">Practicalities  </h2>
 
 <ol>
-<li> Lectures Thursdays 1215pm-2pm, room F&#216;434, Department of Physics</li>
-<li> Lab and exercise sessions Thursdays 215pm-4pm, room F&#216;434, Department of Physics</li> 
+<li> Lectures Thursdays 1015am-12pm, room F&#216;434, Department of Physics</li>
+<li> Lab and exercise sessions Thursdays 1215pm-2pm, room F&#216;434, Department of Physics</li> 
 <li> We plan to work on two projects which will define the content of the course, the format can be agreed upon by the participants</li>
 <li> No exam, only two projects. Each projects counts 1/2 of the final grade. Alternatively, one long project which counts 100% of the final grade</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>
@@ -427,9 +428,8 @@ <h2 id="deep-learning-methods-covered-tentative">Deep learning methods covered,
  <li> Feed forward neural networks and its mathematics (NNs)</li>
  <li> Convolutional neural networks (CNNs)</li>
  <li> Recurrent neural networks (RNNs)</li>
- <li> Graph neural networks</li>
- <li> Transformers</li>  
  <li> Autoencoders and principal component analysis</li>
+ <li> Transformers  (tentative)</li>
 </ol>
 </ol>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -446,6 +446,12 @@ <h2 id="generative-methods">Generative methods </h2>
 </ol>
 </ol>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="reinforcement-learning">Reinforcement learning </h2>
+
+<ol>
+<li> Basics of rerinforcement learning (tentative)</li>
+</ol>
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="both-discriminative-and-generative">Both discriminative and generative </h2>
 
 <ol>
@@ -465,8 +471,6 @@ <h2 id="additional-topics-kernel-regression-gaussian-processes-and-bayesian-stat
 variable).
 </p>
 
-<p>We have not made plans for Reinforcement learning.</p>
-
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="project-paths-overarching-view">Project paths, overarching view </h2>
 

doc/pub/week1/html/week1.html

@@ -150,6 +150,7 @@
                None,
                'deep-learning-methods-covered-tentative'),
               ('Generative methods', 2, None, 'generative-methods'),
+              ('Reinforcement learning', 2, None, 'reinforcement-learning'),
               ('Both discriminative and generative',
                2,
                None,
@@ -477,8 +478,8 @@ <h2 id="overview-of-first-week-january-20-24-2025">Overview of first week, Janua
 <ol>
  <li> Presentation of course</li>
  <li> Discussion of possible projects</li>
- <li> Deep learning methods, mathematics and  review of neural networks</li>
- <li> "Video of lecture at <a href="https://youtu.be/SY57dC46L9o" target="_blank"><tt>https://youtu.be/SY57dC46L9o</tt></a></li>
+ <li> Deep learning methods, mathematics and  review of neural networks
+<!-- o "Video of lecture at <a href="https://youtu.be/SY57dC46L9o" target="_blank"><tt>https://youtu.be/SY57dC46L9o</tt></a> --></li>
  <li> Recommended reading first three weeks: Raschka et al chapter 11 and Goodfellow et al chapters 6 and 7</li>
  <li> Permanent Zoom link for the whole semester is <a href="https://uio.zoom.us/my/mortenhj" target="_blank"><tt>https://uio.zoom.us/my/mortenhj</tt></a></li>
 </ol>
@@ -489,8 +490,8 @@ <h2 id="overview-of-first-week-january-20-24-2025">Overview of first week, Janua
 <h2 id="practicalities">Practicalities  </h2>
 
 <ol>
-<li> Lectures Thursdays 1215pm-2pm, room F&#216;434, Department of Physics</li>
-<li> Lab and exercise sessions Thursdays 215pm-4pm, room F&#216;434, Department of Physics</li> 
+<li> Lectures Thursdays 1015am-12pm, room F&#216;434, Department of Physics</li>
+<li> Lab and exercise sessions Thursdays 1215pm-2pm, room F&#216;434, Department of Physics</li> 
 <li> We plan to work on two projects which will define the content of the course, the format can be agreed upon by the participants</li>
 <li> No exam, only two projects. Each projects counts 1/2 of the final grade. Alternatively, one long project which counts 100% of the final grade</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>
@@ -504,9 +505,8 @@ <h2 id="deep-learning-methods-covered-tentative">Deep learning methods covered,
  <li> Feed forward neural networks and its mathematics (NNs)</li>
  <li> Convolutional neural networks (CNNs)</li>
  <li> Recurrent neural networks (RNNs)</li>
- <li> Graph neural networks</li>
- <li> Transformers</li>  
  <li> Autoencoders and principal component analysis</li>
+ <li> Transformers  (tentative)</li>
 </ol>
 </ol>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -523,6 +523,12 @@ <h2 id="generative-methods">Generative methods </h2>
 </ol>
 </ol>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="reinforcement-learning">Reinforcement learning </h2>
+
+<ol>
+<li> Basics of rerinforcement learning (tentative)</li>
+</ol>
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="both-discriminative-and-generative">Both discriminative and generative </h2>
 
 <ol>
@@ -542,8 +548,6 @@ <h2 id="additional-topics-kernel-regression-gaussian-processes-and-bayesian-stat
 variable).
 </p>
 
-<p>We have not made plans for Reinforcement learning.</p>
-
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="project-paths-overarching-view">Project paths, overarching view </h2>