changes to intro file

doc/src/How2ReadData/How2ReadData.do.txt

@@ -4,23 +4,60 @@ DATE: today
 
 
 !split
-===== What is Machine Learning? =====
+===== Introduction =====
 
-Add definition of definition of machine learning
-
-Machine learning is the science of giving computers the ability to
-learn without being explicitly programmed.  The idea is that there
-exist generic algorithms which can be used to find patterns in a broad
-class of data sets without having to write code specifically for each
-problem. The algorithm will build its own logic based on the data.
+Statistics, data science and machine learning form important fields of
+research in modern science.  They describe how to learn and make
+predictions from data, as well allowing us to extract inportant
+correlations about physical process and the underlying laws of motion
+in large data sets. The latter, big data sets, is now more and more
+frequent in essentially all disciplines, from the traditional Science,
+Technology, Mathematics and Engineering fields to the Humanities and
+the Social Sciences. It has become more and more common to see
+research projects on big data in for example the Social
+Sciences. Having a solid grasp of data analysis and machine learning
+is thus becoming central to scientific computing in many
+fields. Competences and skills within the fields of machine learning
+and scientific computing are nowadays strongly requested by many
+potential employers. The latter cannot be overstated, familiarity with
+machine learning has almost become a prerequisite for many of the most
+exciting employment opportunities, whether they are in bioinformatics,
+life science, physics or finance, in the private or the public
+sector. This author has had several students or met students who have
+been hired recently based on their skills and competences in
+scientific computing and data science, often with marginal knowledge
+of machine learning.
 
 Machine learning is a subfield of computer science, and is closely
 related to computational statistics.  It evolved from the study of
 pattern recognition in artificial intelligence (AI) research, and has
 made contributions to AI tasks like computer vision, natural language
-processing and speech recognition. It has also, especially in later
-years, found applications in a wide variety of other areas, including
-bioinformatics, economy, physics, finance and marketing.
+processing and speech recognition. 
+Machine learning represents the
+science of giving computers the ability to learn without being
+explicitly programmed.  The idea is that there exist generic
+algorithms which can be used to find patterns in a broad class of data
+sets without having to write code specifically for each problem. The
+algorithm will build its own logic based on the data.  
+
+Machine learning is an extremely rich field, in spite of its age. The
+increases we have seen during the last three decades in computational
+capabilities have been followed by a large development in methods and
+techniques for analyzing and handling large date sets, relying heavily
+on statistics, computer science and mathematics.  The field is rather
+new and developing rapidly. Popular software packages written in
+Python for machine learning like _Scikit-learn, Tensorflow and
+PyTorch_, all freely available at their respective GitHub sites,
+encompass communities of developers in the thousands. And the number
+of code developers and contributors keep increasing. Not all the
+algorithms and methods can be given a rigorous mathematical
+justification, opening up thereby large rooms for experimenting
+and trial and error. However, a solid command of linear algebra, multivariate theory, 
+probability theory, statistical data analysis,
+understanding errors and Monte Carlo methods are central elements in a proper understanding of many of 
+algorithms and methods we will discuss. 
+
+
 
 !split
 ===== Types of Machine Learning =====
@@ -31,7 +68,7 @@ In *supervised learning* we know the answer to a problem,
 and let the computer deduce the logic behind it. On the other hand, *unsupervised learning*
 is a method for finding patterns and relationship in data sets without any prior knowledge of the system.
 Some authours also operate with a third category, namely *reinforcement learning*. This is a paradigm 
-of learning inspired by behavioural psychology, where learning is achieved by trial-and-error, 
+of learning inspired by behavioral psychology, where learning is achieved by trial-and-error, 
 solely from rewards and punishment.
 
 Another way to categorize machine learning tasks is to consider the desired output of a system.
@@ -46,15 +83,19 @@ Some of the most common tasks are:
 
 !split
 ===== Different algorithms =====
-In this course we will build our machine learning approach on a statistical foundation, with elements 
+
+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. 
+
+In this series of lectures  we will build our machine learning approach on a statistical foundation, with elements 
 from data analysis, stochastic processes etc  before we proceed with the following machine learning algorithms
 
-o Linear regression and its variants
+o Linear regression and its variants, in essence polynomial regression
 o Decision tree algorithms, from simpler to more complex ones
 o Nearest neighbors models
-o Bayesian statistics 
+o Bayesian statistics and regression
 o Support vector machines and finally various variants of
-o Artifical neural networks
+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. 
@@ -1368,16 +1409,3 @@ p_{ij} \propto \vert m_i-m_j\vert^{-\alpha}\left(c_{ij}+1\right)^{\gamma},
 !et
 where $c_{ij}$ represents the number of  previous interactions that have taken place between $i$ and $j$. The factor $1$ is added in order to ensure that if they have not interacted earlier they can still interact.  Perform similar studies as above with $N=1000$, $\alpha=1.0$ and $\alpha=2.0$ using $\gamma = 0.0, 1.0, 2.0, 3.0$ and $4.0$. Plot the wealth distributions for these cases and try to extract eventual power law tails with and without a saving $\lambda$ in each transaction. Comment your results and compare them with figures 5 and 6 of "Goswami and Sen":"http://www.sciencedirect.com/science/article/pii/S0378437114006967".  
 
-Finally, (this part is optional) if you have time, which features would you add to these models in order to make them even more realistic? 
- 
-===== Background literature =====
-
-  * "V. Pareto, Cours d'economie politique, Lausanne, 1897":"http://www.institutcoppet.org/2012/05/08/cours-deconomie-politique-1896-de-vilfredo-pareto".
-
-  * "M. Patriarca, A. Chakraborti, K. Kaski, Physica A _340_, 334 (2004)":"http://www.sciencedirect.com/science/article/pii/S0378437104004327".
-
-  * "S. Goswami and P. Sen, Physica A _415_, 514 (2014)":"http://www.sciencedirect.com/science/article/pii/S0378437114006967".
-
-
-
-

doc/src/How2ReadData/make.sh

@@ -44,7 +44,7 @@ system doconce split_html $html.html --method=space10
 # Bootstrap style
 html=${name}-bs
 system doconce format html $name --html_style=bootstrap --pygments_html_style=default --html_admon=bootstrap_panel --html_output=$html $opt
-system doconce split_html $html.html --method=split --pagination --nav_button=bottom
+#system doconce split_html $html.html --method=split --pagination --nav_button=bottom
 
 # IPython notebook
 system doconce format ipynb $name $opt