more updates on intro

doc/src/How2ReadData/How2ReadData.do.txt

@@ -106,7 +106,8 @@ calculations is a proper estimation of errors. Here we will also
 discuss famous resampling techniques like the blocking, bootstrapping
 and jackknife methods.
 
-The second part of the material covers several algorithms used in machine learning.
+The second part of the material covers several algorithms used in
+machine learning.
 
 
 
@@ -134,14 +135,17 @@ Some of the most common tasks are:
   * Clustering: Data are divided into groups with certain common traits, without knowing the different groups beforehand.  It is thus a form of unsupervised learning.
 
 
-!split
-===== Different algorithms =====
+The methods we cover have three main topics in common, irrespective of
+whether we deal with supervised or unsupervised learning. The first
+ingredient is normally our data set, the second is a model which is
+normally a function of some parameters. The last ingredient is a
+so-called _cost_ function which allows us to present an estimate on
+how good our model is in reproducing the data it is supposed to train.
 
-The methods we cover have three main topics in common, irrespective of whether we deal with supervised or unsupervised learning. The first ingredient is normally our data set, the second is a model which is normally a function of some parameters. The last ingredient is a so-called _cost_ function which allows us to present an estimate on how good our model is in reproducing the data it is supposed to train. 
-
-Here we will build our machine learning approach on elements of the  statistical foundation discussed above, 
-with elements 
-from data analysis, stochastic processes etc. We will discuss the following machine learning algorithms
+Here we will build our machine learning approach on elements of the
+statistical foundation discussed above, with elements from data
+analysis, stochastic processes etc. We will discuss the following
+machine learning algorithms
 
 o Linear regression and its variants, in essence polynomial regression
 o Decision tree algorithms, from simpler to more complex ones
@@ -151,12 +155,28 @@ o Support vector machines and finally various variants of
 o Artifical neural networks and deep learning
 
 
-Before we proceed however, there are several practicalities with data analysis and software tools we would 
-like to present. These tools will help us in our understanding of various machine learning algorithms. 
+Before we proceed however, there are several practicalities with data
+analysis and software tools we would like to present. These tools will
+help us in our understanding of various machine learning algorithms.
+
+Our emphasis here is on understanding the mathematical aspects of
+different algorithms, however, where possible we will emphasize the
+importance of using available software. We start thus with a hands-on
+and top-down approach machine learning. The aim is thus to start with
+relevant data and use these to introduce statistical data analysis
+concepts and machine learning algorithms before we delve into the
+algorithms themselves. The examples we will use start with a simple
+third-order polynomial with random noise added, and using the Python
+software package "Scikit-learn":"http://scikit-learn.org/stable/" we
+will introduce various machine learning algorithm s to make fits of
+the data data and predictions. We move thereafter to more interesting
+cases such as the simulation of financial transactions or disease
+models. These are examples where we can easily set up the data and
+then use machine learning algorithms using included in for example _scikit_learn_. Another model we
+will consider is the so-called Ising model. Here we will use this
+model to produce data for selected spin configurations and attempt to classify the data. 
+Finally, our last example consists of economic data from the OECD. 
 
-Our emphasis here is on understanding the mathematical aspects of different algorithms, however, where possible 
-we will emphasize the importance of using available software. We start thus with a hands-on and top-down approach machine learning. The aim is thus to start with relevant data and use these to introduce statistical data analysis concepts
-and machine learning algorithms before we delve into the algorithms themselves. 
 
 !split
 ===== Software and needed installations =====