update

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]
 
 
 
+
+