update on intro

doc/src/Introduction/Introduction.do.txt

@@ -3,7 +3,7 @@ AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of
 DATE: today
 
 
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+
 ===== Introduction =====
 
 During the last two decades there has been a swift and amazing
@@ -80,7 +80,7 @@ Carlo methods are central elements in a proper understanding of many
 of algorithms and methods we will discuss.
 
 
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+ 
 ===== Learning outcomes =====
 
 These sets of lectures aim at giving you an overview of central aspects of
@@ -131,13 +131,8 @@ machine learning.
 
 
 
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-
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-===== Machine Learning, short overview  =====
 
 
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 ===== Machine Learning, a small (and probably biased) introduction  =====
 
 
@@ -151,7 +146,7 @@ machines and algorithms are to a large extent developed by humans. The
 insights and knowledge we have about a specific system, play a central
 role when we develop a specific machine learning algorithm. 
 
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 ===== Machine Learning, an extremely rich field  =====
 
 Machine learning is an extremely rich field, in spite of its young
@@ -168,7 +163,7 @@ freely available at their respective GitHub sites, encompass
 communities of developers in the thousands or more. And the number of
 code developers and contributors keeps increasing.
 
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 =====  A multidisciplinary approach =====
 
 Not all the
@@ -183,7 +178,7 @@ of the algorithms and methods we will discuss.
 
 
 
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+
 ===== Types of Machine Learning =====
 
 
@@ -210,7 +205,7 @@ desired output of a system.  Some of the most common tasks are:
 
 
 
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 ===== Essential elements of ML =====
 
 The methods we cover have three main topics in common, irrespective of
@@ -226,12 +221,12 @@ whether we deal with supervised or unsupervised learning.
 
 
 
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 ===== An optimization/minimization problem =====
 
 At the heart of basically all Machine Learning algorithms we will encounter so-called minimization or optimization algorithms. A large family of such methods are so-called _gradient methods_.
 
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 ===== A Frequentist approach to data analysis  =====
 
 When you hear phrases like _predictions and estimations_ and
@@ -260,7 +255,7 @@ used in turn to make estimations and find causations such as given $A$
 what is the likelihood of finding $B$.
 
 
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 ===== What is a good model? =====
 
 In science and engineering we often end up in situations where we want to infer (or learn) a
@@ -284,7 +279,7 @@ is that if we are not specific about what we mean by a *correct* model, there
 could easily be many different models that fit the given data set *equally well*.
 
 
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 ===== What is a good model? Can we define it?  =====
 
 
@@ -309,10 +304,9 @@ may first try the simplest class of models, namely linear models, followed obvio
 
 How to evaluate which model fits best the data is something we will come back to over and over again in these set of lectures.
 
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-===== Practicalities, choice of programming language and other computational issues =====
 
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+
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 ===== Choice of Programming Language =====
 
 Python plays nowadays a central role in the development of machine
@@ -330,7 +324,7 @@ algorithms either as a Python codes or C++ or Fortran (or other languages) codes
 
 
 
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 ===== Data handling, machine learning  and ethical aspects =====
 
 In most of the cases we will study, we will either generate the data

doc/src/Introduction/make.sh

@@ -44,10 +44,10 @@ 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
+system doconce format ipynb $name $opt
 
 
 # Ordinary plain LaTeX document