diff --git a/doc/pub/week1/html/week1-bs.html b/doc/pub/week1/html/week1-bs.html
index bf11fe47..60b11b46 100644
--- a/doc/pub/week1/html/week1-bs.html
+++ b/doc/pub/week1/html/week1-bs.html
@@ -8,8 +8,8 @@
 <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">
@@ -94,7 +94,7 @@
       <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">
@@ -120,7 +120,7 @@
 <!-- ------------------- 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 -->
@@ -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>
 
@@ -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>
 
@@ -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>
 
diff --git a/doc/pub/week1/html/week1-reveal.html b/doc/pub/week1/html/week1-reveal.html
index aded3b99..fcc961fd 100644
--- a/doc/pub/week1/html/week1-reveal.html
+++ b/doc/pub/week1/html/week1-reveal.html
@@ -9,8 +9,8 @@
 <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/ -->
 
@@ -168,7 +168,7 @@
 <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 -->
@@ -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>
 
@@ -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>
@@ -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>
@@ -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>
diff --git a/doc/pub/week1/html/week1-solarized.html b/doc/pub/week1/html/week1-solarized.html
index a850bd8f..66a197e5 100644
--- a/doc/pub/week1/html/week1-solarized.html
+++ b/doc/pub/week1/html/week1-solarized.html
@@ -8,8 +8,8 @@
 <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"/>
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 <script>hljs.initHighlightingOnLoad();</script>
@@ -115,7 +115,7 @@
 
 <!-- ------------------- 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 -->
@@ -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>
 
@@ -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>
 
@@ -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>
 
diff --git a/doc/pub/week1/html/week1.html b/doc/pub/week1/html/week1.html
index 76e08ee8..67c548c5 100644
--- a/doc/pub/week1/html/week1.html
+++ b/doc/pub/week1/html/week1.html
@@ -8,8 +8,8 @@
 <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 {
@@ -192,7 +192,7 @@
 
 <!-- ------------------- 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 -->
@@ -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>
 
@@ -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>
 
@@ -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>
 
diff --git a/doc/pub/week1/ipynb/ipynb-week1-src.tar.gz b/doc/pub/week1/ipynb/ipynb-week1-src.tar.gz
index efb8378d..7d765932 100644
Binary files a/doc/pub/week1/ipynb/ipynb-week1-src.tar.gz and b/doc/pub/week1/ipynb/ipynb-week1-src.tar.gz differ
diff --git a/doc/pub/week1/ipynb/week1.ipynb b/doc/pub/week1/ipynb/week1.ipynb
index d18f5a30..ec395e32 100644
--- a/doc/pub/week1/ipynb/week1.ipynb
+++ b/doc/pub/week1/ipynb/week1.ipynb
@@ -2,31 +2,37 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "75420e80",
-   "metadata": {},
+   "id": "4d8203ce",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)\n",
     "doconce format html week1.do.txt --no_mako -->\n",
-    "<!-- dom:TITLE: January 23-27: Advanced machine learning and data analysis for the physical sciences -->"
+    "<!-- dom:TITLE: January 15-19: Advanced machine learning and data analysis for the physical sciences -->"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4534f2de",
-   "metadata": {},
+   "id": "c955c45b",
+   "metadata": {
+    "editable": true
+   },
    "source": [
-    "# January 23-27: Advanced machine learning and data analysis for the physical sciences\n",
+    "# January 15-19: Advanced machine learning and data analysis for the physical sciences\n",
     "**Morten Hjorth-Jensen**, Department of Physics and Center for Computing in Science Education, University of Oslo, Norway and Department of Physics and Astronomy and Facility for Rare Isotope Beams, Michigan State University, East Lansing, Michigan, USA\n",
     "\n",
-    "Date: **Jan 25, 2023**\n",
+    "Date: **Dec 21, 2023**\n",
     "\n",
     "Copyright 1999-2023, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e02c0321",
-   "metadata": {},
+   "id": "edd35805",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Overview of week first week\n",
     "\n",
@@ -36,15 +42,15 @@
     "\n",
     "3. Discussion of evaluation forms\n",
     "\n",
-    "4. Eventual start with theory discussions on deep learning methods\n",
-    "\n",
-    "[Video of lecture](https://youtube)"
+    "4. Eventual start with theory discussions on deep learning methods"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8146545d",
-   "metadata": {},
+   "id": "092f2b06",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Practicalities and possible projects\n",
     "\n",
@@ -66,35 +72,49 @@
     "\n",
     " * and many other research paths and topics\n",
     "\n",
-    "3. Final oral examination to be agreed upon\n",
+    "3. No exam, only two projects whoch count 1/2 of the final grade each\n",
     "\n",
     "4. All info at the GitHub address <https://github.com/CompPhysics/AdvancedMachineLearning>"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c60db85e",
-   "metadata": {},
+   "id": "91c2e4a5",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Deep learning methods covered\n",
     "\n",
-    "1. Feed forward neural networks (NNs)\n",
+    "1. Deep learning, classics\n",
+    "\n",
+    "a. Feed forward neural networks and its mathematics (NNs)\n",
+    "\n",
+    "b. Convolutional neural networks (CNNs)\n",
+    "\n",
+    "c. Recurrent neural networks (RNNs)\n",
     "\n",
-    "2. Convolutional neural networks (CNNs)\n",
+    "d. Autoencoders and principal component analysis\n",
     "\n",
-    "3. Recurrent neural networks (RNNs)\n",
+    "e. Physics informed neural networks\n",
     "\n",
-    "4. Autoencoders (AEs) and variational autoencoders (VAEe)\n",
+    "6. Deeep learning, generative methods\n",
     "\n",
-    "5. Generative Adversarial Networks (GANs)\n",
+    "a. Boltzmann machines and energy based methods\n",
+    "\n",
+    "b. Autoencodervariational autoencoders (VAEe)\n",
+    "\n",
+    "c. Generative Adversarial Networks (GANs)\n",
     "\n",
     "The [lecture notes](https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html) contain a more in depth discussion of these methods, in particular on neural networks, CNNs and RNNs."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "22e7af8a",
-   "metadata": {},
+   "id": "11fd5de4",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## [Autoencoders and Variational Autoencoders](https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/pub/Autoencoders/ipynb/Autoencoders.ipynb)\n",
     "\n",
@@ -111,8 +131,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8eb95f5e",
-   "metadata": {},
+   "id": "86c69b94",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## [GANs](https://github.com/CompPhysics/AdvancedMachineLearning/blob/main/doc/pub/GenerativeAdversarialNetworks/ipynb/GenerativeAdversarialNetworks.ipynb)\n",
     "\n",
@@ -137,8 +159,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "870434e1",
-   "metadata": {},
+   "id": "2f854fb4",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## [Kernel regression (Gaussian processes) and Bayesian statistics](https://jenfb.github.io/bkmr/overview.html)\n",
     "\n",
@@ -151,8 +175,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d175d68b",
-   "metadata": {},
+   "id": "659e50e0",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## [Physics informed machine learning](https://github.com/maziarraissi/PINNs)\n",
     "\n",
@@ -164,25 +190,7 @@
    ]
   }
  ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.10"
-  }
- },
+ "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 5
 }
diff --git a/doc/pub/week1/pdf/week1.pdf b/doc/pub/week1/pdf/week1.pdf
index b495341e..74caea8f 100644
Binary files a/doc/pub/week1/pdf/week1.pdf and b/doc/pub/week1/pdf/week1.pdf differ
diff --git a/doc/src/week1/week1.do.txt b/doc/src/week1/week1.do.txt
index d1eaa4e6..d510e23b 100644
--- a/doc/src/week1/week1.do.txt
+++ b/doc/src/week1/week1.do.txt
@@ -1,4 +1,4 @@
-TITLE: January 23-27: Advanced machine learning and data analysis for the physical sciences
+TITLE: January 15-19: Advanced machine learning and data analysis for the physical sciences
 AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics and Center for Computing in Science Education, University of Oslo, Norway & Department of Physics and Astronomy and Facility for Rare Isotope Beams, Michigan State University, East Lansing, Michigan, USA
 DATE: today
 
@@ -14,7 +14,6 @@ DATE: today
   o Eventual start with theory discussions on deep learning methods
 !eblock
 
-"Video of lecture":"https://youtube"
 
 !split
 ===== Practicalities and possible projects =====
@@ -28,17 +27,23 @@ o We plan to work on two projects which will define the content of the course, t
  * Physics informed Machine Learning, applications to for example solution of Navier-Stokes equations
  * Bayesian Machine Learning and Gaussian processes
  * and many other research paths and topics
-o Final oral examination to be agreed upon
+o No exam, only two projects whoch count 1/2 of the final grade each
 o All info at the GitHub address URL:"https://github.com/CompPhysics/AdvancedMachineLearning"
 
 !split
 =====  Deep learning methods covered =====
 
-o Feed forward neural networks (NNs)
-o Convolutional neural networks (CNNs)
-o Recurrent neural networks (RNNs)
-o Autoencoders (AEs) and variational autoencoders (VAEe)
-o Generative Adversarial Networks (GANs)
+o Deep learning, classics
+  o Feed forward neural networks and its mathematics (NNs)
+  o Convolutional neural networks (CNNs)
+  o Recurrent neural networks (RNNs)
+  o Autoencoders and principal component analysis
+  o Physics informed neural networks
+o Deeep learning, generative methods
+  o Boltzmann machines and energy based methods
+  o Autoencodervariational autoencoders (VAEe)
+  o Generative Adversarial Networks (GANs)
+
 
 The "lecture notes":"https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html" contain a more in depth discussion of these methods, in particular on neural networks, CNNs and RNNs.