From a51d2ae0f2695a6e280dbd3ede539c1135d5b08f Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Thu, 23 Nov 2023 09:03:57 +0100 Subject: [PATCH] update --- doc/pub/week47/html/._week47-bs004.html | 35 +- doc/pub/week47/html/._week47-bs045.html | 2 +- doc/pub/week47/html/._week47-bs047.html | 1 + doc/pub/week47/html/._week47-bs050.html | 2 +- doc/pub/week47/html/week47-reveal.html | 40 +- doc/pub/week47/html/week47-solarized.html | 40 +- doc/pub/week47/html/week47.html | 40 +- doc/pub/week47/ipynb/ipynb-week47-src.tar.gz | Bin 823750 -> 823750 bytes doc/pub/week47/ipynb/week47.ipynb | 363 ++++++++++--------- doc/src/week47/week47.do.txt | 42 ++- 10 files changed, 384 insertions(+), 181 deletions(-) diff --git a/doc/pub/week47/html/._week47-bs004.html b/doc/pub/week47/html/._week47-bs004.html index f080fcca8..74f7b941b 100644 --- a/doc/pub/week47/html/._week47-bs004.html +++ b/doc/pub/week47/html/._week47-bs004.html @@ -391,18 +391,51 @@ MathJax.Hub.Config({ a decision tree wth different depths and perform a bootstrap aggregate (in this case we perform as many bootstraps as data points \( n \)).

+
- 
import matplotlib.pyplot as plt
+  
# Common imports
+import matplotlib.pyplot as plt
 import numpy as np
 from sklearn.model_selection import train_test_split
 from sklearn.pipeline import make_pipeline
 from sklearn.utils import resample
 from sklearn.tree import DecisionTreeRegressor
+import pandas as pd
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from IPython.display import Image 
+import os
+
+# Where to save the figures and data files
+PROJECT_ROOT_DIR = "Results"
+FIGURE_ID = "Results/FigureFiles"
+DATA_ID = "DataFiles/"
+
+if not os.path.exists(PROJECT_ROOT_DIR):
+    os.mkdir(PROJECT_ROOT_DIR)
+
+if not os.path.exists(FIGURE_ID):
+    os.makedirs(FIGURE_ID)
+
+if not os.path.exists(DATA_ID):
+    os.makedirs(DATA_ID)
+
+def image_path(fig_id):
+    return os.path.join(FIGURE_ID, fig_id)
+
+def data_path(dat_id):
+    return os.path.join(DATA_ID, dat_id)
+
+def save_fig(fig_id):
+    plt.savefig(image_path(fig_id) + ".png", format='png')
+
 
 n = 100
 n_boostraps = 100
diff --git a/doc/pub/week47/html/._week47-bs045.html b/doc/pub/week47/html/._week47-bs045.html
index 00bcf9835..b2cfb3d06 100644
--- a/doc/pub/week47/html/._week47-bs045.html
+++ b/doc/pub/week47/html/._week47-bs045.html
@@ -388,7 +388,7 @@ MathJax.Hub.Config({
 
Other courses on Data science and Machine Learning  at UiO 

 
 
    
-
  1.  FYS5429 Advanced Machine Learning and Data Analysis for the Physical Sciences
    2. 
+
  2.  FYS5429 Advanced Machine Learning and Data Analysis for the Physical Sciences. Discussed deep learning and generative deep learning.
    3. 
 
  3.  FYS5419 Quantum Computing and Quantum Machine Learning
    4. 
 
  4.  STK2100 Machine learning and statistical methods for prediction and classification.
    5.  
 
  5.  IN3050/IN4050 Introduction to Artificial Intelligence and Machine Learning. Introductory course in machine learning and AI with an algorithmic approach.
    6.  
diff --git a/doc/pub/week47/html/._week47-bs047.html b/doc/pub/week47/html/._week47-bs047.html
index e0fded5fa..f31fa6e13 100644
--- a/doc/pub/week47/html/._week47-bs047.html
+++ b/doc/pub/week47/html/._week47-bs047.html
@@ -403,6 +403,7 @@ networks have been proposed, such as
 
  6.  Convolutional neural networks, which are mostly used in image and video data processing, and have also been applied to sequential data such as text processing;
    7. 
 
  7.  Recurrent neural networks, which can process sequential data of variable length and have been widely used in natural language understanding and speech processing;
    8. 
 
  8.  Encoder-decoder framework, which is mostly used for image or sequence generation, such as machine translation, text summarization, and image captioning.
    9. 
+
  9.  Generative deep learning! Recent textbook by David Foster (and obviously many other ones) at https://www.oreilly.com/library/view/generative-deep-learning/9781492041931/"
    10. 
 

 

 
diff --git a/doc/pub/week47/html/._week47-bs050.html b/doc/pub/week47/html/._week47-bs050.html
index de4c95bb2..f5ef8d43d 100644
--- a/doc/pub/week47/html/._week47-bs050.html
+++ b/doc/pub/week47/html/._week47-bs050.html
@@ -387,7 +387,7 @@ MathJax.Hub.Config({
 
 
Boltzmann Machines 

 
-
Why use a generative model rather than the more well known discriminative deep neural networks (DNN)? 

+
Why use a generative model rather than the more well known discriminative deep neural networks (DNN)?  Simplest approach to generative deep learning.

 
 
    
 
  •  Discriminitave methods have several limitations: They are mainly supervised learning methods, thus requiring labeled data. And there are tasks they cannot accomplish, like drawing new examples from an unknown probability distribution.
    • 
diff --git a/doc/pub/week47/html/week47-reveal.html b/doc/pub/week47/html/week47-reveal.html
index cfc7a77de..7375e92a5 100644
--- a/doc/pub/week47/html/week47-reveal.html
+++ b/doc/pub/week47/html/week47-reveal.html
@@ -289,18 +289,51 @@ predictor, averaged over all \( B \) trees.
 a decision tree wth different depths and perform a bootstrap aggregate (in this case we perform as many bootstraps as data points \( n \)).
 
    
 
+
 
 
    
   
    
     
    
       
    
         
    
-  
    import matplotlib.pyplot as plt
+  
    # Common imports
+import matplotlib.pyplot as plt
 import numpy as np
 from sklearn.model_selection import train_test_split
 from sklearn.pipeline import make_pipeline
 from sklearn.utils import resample
 from sklearn.tree import DecisionTreeRegressor
+import pandas as pd
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from IPython.display import Image 
+import os
+
+# Where to save the figures and data files
+PROJECT_ROOT_DIR = "Results"
+FIGURE_ID = "Results/FigureFiles"
+DATA_ID = "DataFiles/"
+
+if not os.path.exists(PROJECT_ROOT_DIR):
+    os.mkdir(PROJECT_ROOT_DIR)
+
+if not os.path.exists(FIGURE_ID):
+    os.makedirs(FIGURE_ID)
+
+if not os.path.exists(DATA_ID):
+    os.makedirs(DATA_ID)
+
+def image_path(fig_id):
+    return os.path.join(FIGURE_ID, fig_id)
+
+def data_path(dat_id):
+    return os.path.join(DATA_ID, dat_id)
+
+def save_fig(fig_id):
+    plt.savefig(image_path(fig_id) + ".png", format='png')
+
 
 n = 100
 n_boostraps = 100
@@ -1675,7 +1708,7 @@ set of hyperparameters and regularization methods.
 
    Other courses on Data science and Machine Learning  at UiO 
    
 
 
      
-
    1.  FYS5429 Advanced Machine Learning and Data Analysis for the Physical Sciences
      2. 
+
    2.  FYS5429 Advanced Machine Learning and Data Analysis for the Physical Sciences. Discussed deep learning and generative deep learning.
      3. 
 
    3.  FYS5419 Quantum Computing and Quantum Machine Learning
      4. 
 
    4.  STK2100 Machine learning and statistical methods for prediction and classification.
      5.  
 
    5.  IN3050/IN4050 Introduction to Artificial Intelligence and Machine Learning. Introductory course in machine learning and AI with an algorithmic approach.
      6.  
@@ -1715,6 +1748,7 @@ networks have been proposed, such as
 
    6.  Convolutional neural networks, which are mostly used in image and video data processing, and have also been applied to sequential data such as text processing;
      7. 
 
    7.  Recurrent neural networks, which can process sequential data of variable length and have been widely used in natural language understanding and speech processing;
      8. 
 
    8.  Encoder-decoder framework, which is mostly used for image or sequence generation, such as machine translation, text summarization, and image captioning.
      9. 
+
    9.  Generative deep learning! Recent textbook by David Foster (and obviously many other ones) at https://www.oreilly.com/library/view/generative-deep-learning/9781492041931/"
      10. 
 
    
 
 
@@ -1765,7 +1799,7 @@ One of the major reasons is that they can be stacked layer-wise to build deep ne
 
    
 
    Boltzmann Machines 
    
 
-
    Why use a generative model rather than the more well known discriminative deep neural networks (DNN)? 
    
+
    Why use a generative model rather than the more well known discriminative deep neural networks (DNN)?  Simplest approach to generative deep learning.
    
 
 
      
 
    •  Discriminitave methods have several limitations: They are mainly supervised learning methods, thus requiring labeled data. And there are tasks they cannot accomplish, like drawing new examples from an unknown probability distribution.
      • 
diff --git a/doc/pub/week47/html/week47-solarized.html b/doc/pub/week47/html/week47-solarized.html
index fecfb18d5..04261e4a7 100644
--- a/doc/pub/week47/html/week47-solarized.html
+++ b/doc/pub/week47/html/week47-solarized.html
@@ -411,18 +411,51 @@ predictor, averaged over all \( B \) trees.
 a decision tree wth different depths and perform a bootstrap aggregate (in this case we perform as many bootstraps as data points \( n \)).
 
      
 
+
 
 
      
   
      
     
      
       
      
         
      
-  
      import matplotlib.pyplot as plt
+  
      # Common imports
+import matplotlib.pyplot as plt
 import numpy as np
 from sklearn.model_selection import train_test_split
 from sklearn.pipeline import make_pipeline
 from sklearn.utils import resample
 from sklearn.tree import DecisionTreeRegressor
+import pandas as pd
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from IPython.display import Image 
+import os
+
+# Where to save the figures and data files
+PROJECT_ROOT_DIR = "Results"
+FIGURE_ID = "Results/FigureFiles"
+DATA_ID = "DataFiles/"
+
+if not os.path.exists(PROJECT_ROOT_DIR):
+    os.mkdir(PROJECT_ROOT_DIR)
+
+if not os.path.exists(FIGURE_ID):
+    os.makedirs(FIGURE_ID)
+
+if not os.path.exists(DATA_ID):
+    os.makedirs(DATA_ID)
+
+def image_path(fig_id):
+    return os.path.join(FIGURE_ID, fig_id)
+
+def data_path(dat_id):
+    return os.path.join(DATA_ID, dat_id)
+
+def save_fig(fig_id):
+    plt.savefig(image_path(fig_id) + ".png", format='png')
+
 
 n = 100
 n_boostraps = 100
@@ -1655,7 +1688,7 @@ set of hyperparameters and regularization methods.
 
      Other courses on Data science and Machine Learning  at UiO 
      
 
 
        
-
      1.  FYS5429 Advanced Machine Learning and Data Analysis for the Physical Sciences
        2. 
+
      2.  FYS5429 Advanced Machine Learning and Data Analysis for the Physical Sciences. Discussed deep learning and generative deep learning.
        3. 
 
      3.  FYS5419 Quantum Computing and Quantum Machine Learning
        4. 
 
      4.  STK2100 Machine learning and statistical methods for prediction and classification.
        5.  
 
      5.  IN3050/IN4050 Introduction to Artificial Intelligence and Machine Learning. Introductory course in machine learning and AI with an algorithmic approach.
        6.  
@@ -1691,6 +1724,7 @@ networks have been proposed, such as
 
      6.  Convolutional neural networks, which are mostly used in image and video data processing, and have also been applied to sequential data such as text processing;
        7. 
 
      7.  Recurrent neural networks, which can process sequential data of variable length and have been widely used in natural language understanding and speech processing;
        8. 
 
      8.  Encoder-decoder framework, which is mostly used for image or sequence generation, such as machine translation, text summarization, and image captioning.
        9. 
+
      9.  Generative deep learning! Recent textbook by David Foster (and obviously many other ones) at https://www.oreilly.com/library/view/generative-deep-learning/9781492041931/"
        10. 
 
      
 









      
 
      Types of Machine Learning, a repetition 
      
@@ -1734,7 +1768,7 @@ One of the major reasons is that they can be stacked layer-wise to build deep ne
 









      
 
      Boltzmann Machines 
      
 
-
      Why use a generative model rather than the more well known discriminative deep neural networks (DNN)? 
      
+
      Why use a generative model rather than the more well known discriminative deep neural networks (DNN)?  Simplest approach to generative deep learning.
      
 
 
        
 
      •  Discriminitave methods have several limitations: They are mainly supervised learning methods, thus requiring labeled data. And there are tasks they cannot accomplish, like drawing new examples from an unknown probability distribution.
        • 
diff --git a/doc/pub/week47/html/week47.html b/doc/pub/week47/html/week47.html
index 424606c5a..c2717ae1a 100644
--- a/doc/pub/week47/html/week47.html
+++ b/doc/pub/week47/html/week47.html
@@ -488,18 +488,51 @@ predictor, averaged over all \( B \) trees.
 a decision tree wth different depths and perform a bootstrap aggregate (in this case we perform as many bootstraps as data points \( n \)).
 
        
 
+
 
 
        
   
        
     
        
       
        
         
        
-  
        import matplotlib.pyplot as plt
+  
        # Common imports
+import matplotlib.pyplot as plt
 import numpy as np
 from sklearn.model_selection import train_test_split
 from sklearn.pipeline import make_pipeline
 from sklearn.utils import resample
 from sklearn.tree import DecisionTreeRegressor
+import pandas as pd
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from IPython.display import Image 
+import os
+
+# Where to save the figures and data files
+PROJECT_ROOT_DIR = "Results"
+FIGURE_ID = "Results/FigureFiles"
+DATA_ID = "DataFiles/"
+
+if not os.path.exists(PROJECT_ROOT_DIR):
+    os.mkdir(PROJECT_ROOT_DIR)
+
+if not os.path.exists(FIGURE_ID):
+    os.makedirs(FIGURE_ID)
+
+if not os.path.exists(DATA_ID):
+    os.makedirs(DATA_ID)
+
+def image_path(fig_id):
+    return os.path.join(FIGURE_ID, fig_id)
+
+def data_path(dat_id):
+    return os.path.join(DATA_ID, dat_id)
+
+def save_fig(fig_id):
+    plt.savefig(image_path(fig_id) + ".png", format='png')
+
 
 n = 100
 n_boostraps = 100
@@ -1732,7 +1765,7 @@ set of hyperparameters and regularization methods.
 
        Other courses on Data science and Machine Learning  at UiO 
        
 
 
          
-
        1.  FYS5429 Advanced Machine Learning and Data Analysis for the Physical Sciences
          2. 
+
        2.  FYS5429 Advanced Machine Learning and Data Analysis for the Physical Sciences. Discussed deep learning and generative deep learning.
          3. 
 
        3.  FYS5419 Quantum Computing and Quantum Machine Learning
          4. 
 
        4.  STK2100 Machine learning and statistical methods for prediction and classification.
          5.  
 
        5.  IN3050/IN4050 Introduction to Artificial Intelligence and Machine Learning. Introductory course in machine learning and AI with an algorithmic approach.
          6.  
@@ -1768,6 +1801,7 @@ networks have been proposed, such as
 
        6.  Convolutional neural networks, which are mostly used in image and video data processing, and have also been applied to sequential data such as text processing;
          7. 
 
        7.  Recurrent neural networks, which can process sequential data of variable length and have been widely used in natural language understanding and speech processing;
          8. 
 
        8.  Encoder-decoder framework, which is mostly used for image or sequence generation, such as machine translation, text summarization, and image captioning.
          9. 
+
        9.  Generative deep learning! Recent textbook by David Foster (and obviously many other ones) at https://www.oreilly.com/library/view/generative-deep-learning/9781492041931/"
          10. 
 
        
 









        
 
        Types of Machine Learning, a repetition 
        
@@ -1811,7 +1845,7 @@ One of the major reasons is that they can be stacked layer-wise to build deep ne
 









        
 
        Boltzmann Machines 
        
 
-
        Why use a generative model rather than the more well known discriminative deep neural networks (DNN)? 
        
+
        Why use a generative model rather than the more well known discriminative deep neural networks (DNN)?  Simplest approach to generative deep learning.
        
 
 
          
 
        •  Discriminitave methods have several limitations: They are mainly supervised learning methods, thus requiring labeled data. And there are tasks they cannot accomplish, like drawing new examples from an unknown probability distribution.
          • 
diff --git a/doc/pub/week47/ipynb/ipynb-week47-src.tar.gz b/doc/pub/week47/ipynb/ipynb-week47-src.tar.gz
index d45db9e6bf3d1ddc17916bf6c8c4555f25dd82e0..f1d1b4b31a631125fab00b88e58b543cd0f866c3 100644
GIT binary patch
delta 54
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IcAotc0g=BAn*aa+

delta 54
zcmX@M*yz||BX;?24u(*Rm`3(icE(nArdD?5R(6(FcGgyQwpMnaAV(`ZXDd5bD?9gA
IcAotc0hgc+zW@LL

diff --git a/doc/pub/week47/ipynb/week47.ipynb b/doc/pub/week47/ipynb/week47.ipynb
index 737f5833a..7e980fac8 100644
--- a/doc/pub/week47/ipynb/week47.ipynb
+++ b/doc/pub/week47/ipynb/week47.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "c7f2117b",
+   "id": "795eb464",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "825c2a47",
+   "id": "d1aa4009",
    "metadata": {
     "editable": true
    },
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0d008f8e",
+   "id": "cb6aa61c",
    "metadata": {
     "editable": true
    },
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "33656118",
+   "id": "15ea90a2",
    "metadata": {
     "editable": true
    },
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6cd3f1f3",
+   "id": "9fa05123",
    "metadata": {
     "editable": true
    },
@@ -117,7 +117,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6a774f1c",
+   "id": "ee547e51",
    "metadata": {
     "editable": true
    },
@@ -131,7 +131,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "5862ca1b",
+   "id": "c5508f3e",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -140,13 +140,44 @@
    "source": [
     "%matplotlib inline\n",
     "\n",
-    "\n",
+    "# Common imports\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.pipeline import make_pipeline\n",
     "from sklearn.utils import resample\n",
     "from sklearn.tree import DecisionTreeRegressor\n",
+    "import pandas as pd\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "from IPython.display import Image \n",
+    "import os\n",
+    "\n",
+    "# Where to save the figures and data files\n",
+    "PROJECT_ROOT_DIR = \"Results\"\n",
+    "FIGURE_ID = \"Results/FigureFiles\"\n",
+    "DATA_ID = \"DataFiles/\"\n",
+    "\n",
+    "if not os.path.exists(PROJECT_ROOT_DIR):\n",
+    "    os.mkdir(PROJECT_ROOT_DIR)\n",
+    "\n",
+    "if not os.path.exists(FIGURE_ID):\n",
+    "    os.makedirs(FIGURE_ID)\n",
+    "\n",
+    "if not os.path.exists(DATA_ID):\n",
+    "    os.makedirs(DATA_ID)\n",
+    "\n",
+    "def image_path(fig_id):\n",
+    "    return os.path.join(FIGURE_ID, fig_id)\n",
+    "\n",
+    "def data_path(dat_id):\n",
+    "    return os.path.join(DATA_ID, dat_id)\n",
+    "\n",
+    "def save_fig(fig_id):\n",
+    "    plt.savefig(image_path(fig_id) + \".png\", format='png')\n",
+    "\n",
     "\n",
     "n = 100\n",
     "n_boostraps = 100\n",
@@ -202,7 +233,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09a6717c",
+   "id": "a5e9cd81",
    "metadata": {
     "editable": true
    },
@@ -225,7 +256,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "43222d10",
+   "id": "fd4f2752",
    "metadata": {
     "editable": true
    },
@@ -237,7 +268,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "52a18bb5",
+   "id": "faa2b01b",
    "metadata": {
     "editable": true
    },
@@ -262,7 +293,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e54c9923",
+   "id": "0c04c2a3",
    "metadata": {
     "editable": true
    },
@@ -288,7 +319,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6b7b8f64",
+   "id": "11833662",
    "metadata": {
     "editable": true
    },
@@ -299,7 +330,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "a4a2b51c",
+   "id": "d0ddbeee",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -371,7 +402,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "395b3a90",
+   "id": "d8f43bda",
    "metadata": {
     "editable": true
    },
@@ -387,7 +418,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c035f0c1",
+   "id": "95aee084",
    "metadata": {
     "editable": true
    },
@@ -398,7 +429,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "ab6ad020",
+   "id": "316f2b3a",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -413,7 +444,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "a1472251",
+   "id": "e41da901",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -431,7 +462,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "51b7ace2",
+   "id": "b1ded974",
    "metadata": {
     "editable": true
    },
@@ -451,7 +482,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8d462537",
+   "id": "5c4a65da",
    "metadata": {
     "editable": true
    },
@@ -465,7 +496,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9fb8e1b0",
+   "id": "995a1a54",
    "metadata": {
     "editable": true
    },
@@ -477,7 +508,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ee0ec6f5",
+   "id": "a6dea1ae",
    "metadata": {
     "editable": true
    },
@@ -494,7 +525,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "64e7803c",
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@@ -506,7 +537,7 @@
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@@ -520,7 +551,7 @@
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@@ -532,7 +563,7 @@
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@@ -545,7 +576,7 @@
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@@ -557,7 +588,7 @@
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@@ -567,7 +598,7 @@
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@@ -595,7 +626,7 @@
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@@ -611,7 +642,7 @@
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@@ -623,7 +654,7 @@
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@@ -634,7 +665,7 @@
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@@ -646,7 +677,7 @@
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@@ -656,7 +687,7 @@
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@@ -668,7 +699,7 @@
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@@ -678,7 +709,7 @@
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@@ -690,7 +721,7 @@
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@@ -700,7 +731,7 @@
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@@ -712,7 +743,7 @@
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@@ -726,7 +757,7 @@
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@@ -742,7 +773,7 @@
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@@ -754,7 +785,7 @@
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@@ -770,7 +801,7 @@
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@@ -782,7 +813,7 @@
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@@ -792,7 +823,7 @@
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@@ -804,7 +835,7 @@
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@@ -816,7 +847,7 @@
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@@ -828,7 +859,7 @@
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@@ -839,7 +870,7 @@
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@@ -851,7 +882,7 @@
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@@ -862,7 +893,7 @@
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@@ -874,7 +905,7 @@
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@@ -884,7 +915,7 @@
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@@ -896,7 +927,7 @@
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@@ -908,7 +939,7 @@
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@@ -920,7 +951,7 @@
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@@ -932,7 +963,7 @@
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@@ -942,7 +973,7 @@
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@@ -954,7 +985,7 @@
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@@ -964,7 +995,7 @@
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@@ -976,7 +1007,7 @@
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@@ -986,7 +1017,7 @@
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@@ -998,7 +1029,7 @@
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@@ -1008,7 +1039,7 @@
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@@ -1020,7 +1051,7 @@
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@@ -1030,7 +1061,7 @@
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@@ -1042,7 +1073,7 @@
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@@ -1062,7 +1093,7 @@
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   {
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@@ -1074,7 +1105,7 @@
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@@ -1084,7 +1115,7 @@
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@@ -1100,7 +1131,7 @@
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@@ -1112,7 +1143,7 @@
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@@ -1140,7 +1171,7 @@
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@@ -1153,7 +1184,7 @@
   {
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-   "id": "31bc0132",
+   "id": "1ca70ef7",
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@@ -1178,7 +1209,7 @@
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@@ -1196,7 +1227,7 @@
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@@ -1209,7 +1240,7 @@
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@@ -1221,7 +1252,7 @@
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@@ -1231,7 +1262,7 @@
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@@ -1243,7 +1274,7 @@
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@@ -1253,7 +1284,7 @@
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@@ -1265,7 +1296,7 @@
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@@ -1278,7 +1309,7 @@
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@@ -1290,7 +1321,7 @@
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@@ -1302,7 +1333,7 @@
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@@ -1314,7 +1345,7 @@
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@@ -1324,7 +1355,7 @@
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-   "id": "d07aae49",
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@@ -1336,7 +1367,7 @@
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-   "id": "217b0ec0",
+   "id": "82234c63",
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@@ -1346,7 +1377,7 @@
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+   "id": "d886e0d6",
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@@ -1362,7 +1393,7 @@
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+   "id": "3bc6ddc2",
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@@ -1374,7 +1405,7 @@
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@@ -1395,7 +1426,7 @@
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@@ -1406,7 +1437,7 @@
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-   "id": "8bc581d3",
+   "id": "bd265d3f",
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@@ -1458,7 +1489,7 @@
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@@ -1469,7 +1500,7 @@
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-   "id": "4519bd6e",
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@@ -1520,7 +1551,7 @@
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@@ -1543,7 +1574,7 @@
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@@ -1554,7 +1585,7 @@
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-   "id": "389441a3",
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@@ -1606,7 +1637,7 @@
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@@ -1619,7 +1650,7 @@
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-   "id": "c40249c8",
+   "id": "241155b2",
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@@ -1682,7 +1713,7 @@
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@@ -1692,7 +1723,7 @@
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@@ -1707,7 +1738,7 @@
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@@ -1723,7 +1754,7 @@
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@@ -1748,7 +1779,7 @@
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@@ -1777,7 +1808,7 @@
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@@ -1793,7 +1824,7 @@
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@@ -1818,7 +1849,7 @@
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@@ -1865,7 +1896,7 @@
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@@ -1901,7 +1932,7 @@
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@@ -1924,7 +1955,7 @@
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@@ -1947,7 +1978,7 @@
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@@ -1967,7 +1998,7 @@
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@@ -1983,7 +2014,7 @@
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@@ -2015,7 +2046,7 @@
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@@ -2037,7 +2068,7 @@
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@@ -2062,7 +2093,7 @@
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@@ -2080,14 +2111,14 @@
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-   "id": "e2c01a1c",
+   "id": "474deaee",
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    "source": [
     "## Other courses on Data science and Machine Learning  at UiO\n",
     "\n",
-    "1. [FYS5429 Advanced Machine Learning and Data Analysis for the Physical Sciences](https://www.uio.no/studier/emner/matnat/fys/FYS5429/index-eng.html)\n",
+    "1. [FYS5429 Advanced Machine Learning and Data Analysis for the Physical Sciences](https://www.uio.no/studier/emner/matnat/fys/FYS5429/index-eng.html). Discussed deep learning and generative deep learning.\n",
     "\n",
     "2. [FYS5419 Quantum Computing and Quantum Machine Learning](https://www.uio.no/studier/emner/matnat/fys/FYS5419/index-eng.html)\n",
     "\n",
@@ -2108,7 +2139,7 @@
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@@ -2143,12 +2174,14 @@
     "\n",
     "2. Recurrent neural networks, which can process sequential data of variable length and have been widely used in natural language understanding and speech processing;\n",
     "\n",
-    "3. Encoder-decoder framework, which is mostly used for image or sequence generation, such as machine translation, text summarization, and image captioning."
+    "3. Encoder-decoder framework, which is mostly used for image or sequence generation, such as machine translation, text summarization, and image captioning.\n",
+    "\n",
+    "4. **Generative deep learning**! Recent textbook by David Foster (and obviously many other ones) at \""
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "82fed487",
+   "id": "6815cca1",
    "metadata": {
     "editable": true
    },
@@ -2177,7 +2210,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d1496484",
+   "id": "83942b75",
    "metadata": {
     "editable": true
    },
@@ -2194,14 +2227,14 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9fcb0478",
+   "id": "28aae5b7",
    "metadata": {
     "editable": true
    },
    "source": [
     "## Boltzmann Machines\n",
     "\n",
-    "Why use a generative model rather than the more well known discriminative deep neural networks (DNN)? \n",
+    "Why use a generative model rather than the more well known discriminative deep neural networks (DNN)?  **Simplest approach to generative deep learning**.\n",
     "\n",
     "* Discriminitave methods have several limitations: They are mainly supervised learning methods, thus requiring labeled data. And there are tasks they cannot accomplish, like drawing new examples from an unknown probability distribution.\n",
     "\n",
@@ -2216,7 +2249,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5eb0d3e2",
+   "id": "72e08b43",
    "metadata": {
     "editable": true
    },
@@ -2234,7 +2267,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "665c0ef9",
+   "id": "e9bc5428",
    "metadata": {
     "editable": true
    },
@@ -2257,7 +2290,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4403ff05",
+   "id": "1e002295",
    "metadata": {
     "editable": true
    },
@@ -2276,7 +2309,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cfee8419",
+   "id": "4488ef6f",
    "metadata": {
     "editable": true
    },
@@ -2292,7 +2325,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "04e219f3",
+   "id": "b034bad4",
    "metadata": {
     "editable": true
    },
@@ -2307,7 +2340,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "865be2c7",
+   "id": "59c3525f",
    "metadata": {
     "editable": true
    },
@@ -2331,7 +2364,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ad081b40",
+   "id": "bebccfe7",
    "metadata": {
     "editable": true
    },
@@ -2343,7 +2376,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f09b6fa7",
+   "id": "9f9b56d5",
    "metadata": {
     "editable": true
    },
@@ -2361,7 +2394,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "18ae7c01",
+   "id": "6508ccbb",
    "metadata": {
     "editable": true
    },
@@ -2371,7 +2404,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a88f7dbd",
+   "id": "686e8551",
    "metadata": {
     "editable": true
    },
@@ -2389,7 +2422,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f3e9df0a",
+   "id": "80ad6463",
    "metadata": {
     "editable": true
    },
@@ -2399,7 +2432,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4fb63226",
+   "id": "e7c6595e",
    "metadata": {
     "editable": true
    },
@@ -2418,7 +2451,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "979d4b88",
+   "id": "24eeaa38",
    "metadata": {
     "editable": true
    },
@@ -2430,7 +2463,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "657ea136",
+   "id": "e29eb8af",
    "metadata": {
     "editable": true
    },
@@ -2444,7 +2477,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f74cfe5",
+   "id": "f0159edd",
    "metadata": {
     "editable": true
    },
@@ -2459,7 +2492,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d2ceac17",
+   "id": "a35dca77",
    "metadata": {
     "editable": true
    },
@@ -2477,7 +2510,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c10dc2df",
+   "id": "b6b0cc70",
    "metadata": {
     "editable": true
    },
@@ -2491,7 +2524,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d0231e81",
+   "id": "ec274a69",
    "metadata": {
     "editable": true
    },
@@ -2509,7 +2542,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "93975174",
+   "id": "81011851",
    "metadata": {
     "editable": true
    },
@@ -2533,7 +2566,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f0764260",
+   "id": "44686f56",
    "metadata": {
     "editable": true
    },
@@ -2571,7 +2604,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f38043a9",
+   "id": "2e493e53",
    "metadata": {
     "editable": true
    },
@@ -2594,7 +2627,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6011b6cd",
+   "id": "0bc48b90",
    "metadata": {
     "editable": true
    },
@@ -2630,7 +2663,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "51df0ece",
+   "id": "d16547e3",
    "metadata": {
     "editable": true
    },
@@ -2651,7 +2684,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ae9499ab",
+   "id": "2de4ecb9",
    "metadata": {
     "editable": true
    },
@@ -2673,7 +2706,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3ad34881",
+   "id": "f55cdb1f",
    "metadata": {
     "editable": true
    },
@@ -2693,7 +2726,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5f414072",
+   "id": "027e5a53",
    "metadata": {
     "editable": true
    },
@@ -2708,7 +2741,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1fe2970f",
+   "id": "6eafe153",
    "metadata": {
     "editable": true
    },
@@ -2726,7 +2759,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ddc712ca",
+   "id": "0c120668",
    "metadata": {
     "editable": true
    },
@@ -2756,7 +2789,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd450047",
+   "id": "ce1d1942",
    "metadata": {
     "editable": true
    },
@@ -2785,7 +2818,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f54dc4f7",
+   "id": "92e30af9",
    "metadata": {
     "editable": true
    },
@@ -2808,7 +2841,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "af592ffc",
+   "id": "a76f5dfb",
    "metadata": {
     "editable": true
    },
@@ -2839,7 +2872,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0a745b16",
+   "id": "225aac31",
    "metadata": {
     "editable": true
    },
@@ -2862,7 +2895,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "08e6c05f",
+   "id": "b6842272",
    "metadata": {
     "editable": true
    },
@@ -2880,7 +2913,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6836009e",
+   "id": "85f09cc0",
    "metadata": {
     "editable": true
    },
@@ -2903,7 +2936,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "330b92f8",
+   "id": "befe6397",
    "metadata": {
     "editable": true
    },
@@ -2924,7 +2957,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b1614d42",
+   "id": "6625cb3c",
    "metadata": {
     "editable": true
    },
@@ -2940,7 +2973,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d64127e7",
+   "id": "f247452c",
    "metadata": {
     "editable": true
    },
@@ -2960,7 +2993,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3821ae71",
+   "id": "bc953bac",
    "metadata": {
     "editable": true
    },
diff --git a/doc/src/week47/week47.do.txt b/doc/src/week47/week47.do.txt
index 0ff631c8f..4d0ce5c9e 100644
--- a/doc/src/week47/week47.do.txt
+++ b/doc/src/week47/week47.do.txt
@@ -71,14 +71,46 @@ predictor, averaged over all $B$ trees.
 
 Let us bring up our good old boostrap example from the linear regression lectures. We change the linerar regression algorithm with
 a decision tree wth different depths and perform a bootstrap aggregate (in this case we perform as many bootstraps as data points $n$).
-!bc pycod
 
+!bc pycod
+# Common imports
 import matplotlib.pyplot as plt
 import numpy as np
 from sklearn.model_selection import train_test_split
 from sklearn.pipeline import make_pipeline
 from sklearn.utils import resample
 from sklearn.tree import DecisionTreeRegressor
+import pandas as pd
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from IPython.display import Image 
+import os
+
+# Where to save the figures and data files
+PROJECT_ROOT_DIR = "Results"
+FIGURE_ID = "Results/FigureFiles"
+DATA_ID = "DataFiles/"
+
+if not os.path.exists(PROJECT_ROOT_DIR):
+    os.mkdir(PROJECT_ROOT_DIR)
+
+if not os.path.exists(FIGURE_ID):
+    os.makedirs(FIGURE_ID)
+
+if not os.path.exists(DATA_ID):
+    os.makedirs(DATA_ID)
+
+def image_path(fig_id):
+    return os.path.join(FIGURE_ID, fig_id)
+
+def data_path(dat_id):
+    return os.path.join(DATA_ID, dat_id)
+
+def save_fig(fig_id):
+    plt.savefig(image_path(fig_id) + ".png", format='png')
+
 
 n = 100
 n_boostraps = 100
@@ -1155,7 +1187,7 @@ o Jackknife and many other
 ===== Other courses on Data science and Machine Learning  at UiO =====
 
 
-o "FYS5429 Advanced Machine Learning and Data Analysis for the Physical Sciences":"https://www.uio.no/studier/emner/matnat/fys/FYS5429/index-eng.html"
+o "FYS5429 Advanced Machine Learning and Data Analysis for the Physical Sciences":"https://www.uio.no/studier/emner/matnat/fys/FYS5429/index-eng.html". Discussed deep learning and generative deep learning.
 o "FYS5419 Quantum Computing and Quantum Machine Learning":"https://www.uio.no/studier/emner/matnat/fys/FYS5419/index-eng.html"
 o "STK2100 Machine learning and statistical methods for prediction and classification":"http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html". 
 o "IN3050/IN4050 Introduction to Artificial Intelligence and Machine Learning":"https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html". Introductory course in machine learning and AI with an algorithmic approach. 
@@ -1187,7 +1219,7 @@ networks have been proposed, such as
 o Convolutional neural networks, which are mostly used in image and video data processing, and have also been applied to sequential data such as text processing;
 o Recurrent neural networks, which can process sequential data of variable length and have been widely used in natural language understanding and speech processing;
 o Encoder-decoder framework, which is mostly used for image or sequence generation, such as machine translation, text summarization, and image captioning.
-
+o _Generative deep learning_! Recent textbook by David Foster (and obviously many other ones) at URL:"https://www.oreilly.com/library/view/generative-deep-learning/9781492041931/""
 !split
 ===== Types of Machine Learning, a repetition =====
 
@@ -1227,7 +1259,7 @@ Furthermore, they have been used to solve complicated "quantum mechanical many-p
 !split
 ===== Boltzmann Machines =====
 
-Why use a generative model rather than the more well known discriminative deep neural networks (DNN)? 
+Why use a generative model rather than the more well known discriminative deep neural networks (DNN)?  _Simplest approach to generative deep learning_.
 
 * Discriminitave methods have several limitations: They are mainly supervised learning methods, thus requiring labeled data. And there are tasks they cannot accomplish, like drawing new examples from an unknown probability distribution.
 
@@ -1734,3 +1766,5 @@ FIGURE: [figures/Nebbdyr2.png, width=500 frac=0.6]
 
 
 
+
+