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update on intro

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mhjensen
2020-09-19 22:29:09 +02:00
parent 666100d74a
commit 273810ff7b
9 changed files with 270 additions and 421 deletions
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doc/pub/Introduction/html/Introduction.html
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@@ -42,29 +42,23 @@ div { text-align: justify; text-justify: inter-word; }
{'highest level': 2,
'sections': [('Introduction', 2, None, '___sec0'),
('Learning outcomes', 2, None, '___sec1'),
('Machine Learning, short overview', 2, None, '___sec2'),
('Machine Learning, a small (and probably biased) introduction',
2,
None,
'___sec3'),
('Machine Learning, an extremely rich field', 2, None, '___sec4'),
('A multidisciplinary approach', 2, None, '___sec5'),
('Types of Machine Learning', 2, None, '___sec6'),
('Essential elements of ML', 2, None, '___sec7'),
('An optimization/minimization problem', 2, None, '___sec8'),
('A Frequentist approach to data analysis', 2, None, '___sec9'),
('What is a good model?', 2, None, '___sec10'),
('What is a good model? Can we define it?', 2, None, '___sec11'),
('Practicalities, choice of programming language and other '
'computational issues',
2,
None,
'___sec12'),
('Choice of Programming Language', 2, None, '___sec13'),
'___sec2'),
('Machine Learning, an extremely rich field', 2, None, '___sec3'),
('A multidisciplinary approach', 2, None, '___sec4'),
('Types of Machine Learning', 2, None, '___sec5'),
('Essential elements of ML', 2, None, '___sec6'),
('An optimization/minimization problem', 2, None, '___sec7'),
('A Frequentist approach to data analysis', 2, None, '___sec8'),
('What is a good model?', 2, None, '___sec9'),
('What is a good model? Can we define it?', 2, None, '___sec10'),
('Choice of Programming Language', 2, None, '___sec11'),
('Data handling, machine learning and ethical aspects',
2,
None,
'___sec14')]}
'___sec12')]}
end of tocinfo -->
<body>
@@ -106,10 +100,8 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Aug 20, 2020</h4></center> <!-- date -->
<center><h4>Sep 19, 2020</h4></center> <!-- date -->
<br>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec0">Introduction </h2>
@@ -192,9 +184,6 @@ 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.
<p>
<!-- !split -->
<h2 id="___sec1">Learning outcomes </h2>
<p>
@@ -249,18 +238,7 @@ and the jackknife methods and the infamous bias-variance tradeoff.
The second part of the material covers several algorithms used in
machine learning.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec2">Machine Learning, short overview </h2>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec3">Machine Learning, a small (and probably biased) introduction </h2>
<h2 id="___sec2">Machine Learning, a small (and probably biased) introduction </h2>
<p>
Ideally, machine learning represents the science of giving computers
@@ -273,10 +251,7 @@ 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.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec4">Machine Learning, an extremely rich field </h2>
<h2 id="___sec3">Machine Learning, an extremely rich field </h2>
<p>
Machine learning is an extremely rich field, in spite of its young
@@ -293,10 +268,7 @@ 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.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec5">A multidisciplinary approach </h2>
<h2 id="___sec4">A multidisciplinary approach </h2>
<p>
Not all the
@@ -308,10 +280,7 @@ theory, statistical data analysis, understanding errors and Monte
Carlo methods are central elements in a proper understanding of many
of the algorithms and methods we will discuss.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec6">Types of Machine Learning </h2>
<h2 id="___sec5">Types of Machine Learning </h2>
<p>
The approaches to machine learning are many, but are often split into
@@ -339,10 +308,7 @@ desired output of a system. Some of the most common tasks are:
<!-- !epop -->
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec7">Essential elements of ML </h2>
<h2 id="___sec6">Essential elements of ML </h2>
<p>
The methods we cover have three main topics in common, irrespective of
@@ -357,18 +323,12 @@ whether we deal with supervised or unsupervised learning.
<!-- !epop -->
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec8">An optimization/minimization problem </h2>
<h2 id="___sec7">An optimization/minimization problem </h2>
<p>
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 <b>gradient methods</b>.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec9">A Frequentist approach to data analysis </h2>
<h2 id="___sec8">A Frequentist approach to data analysis </h2>
<p>
When you hear phrases like <b>predictions and estimations</b> and
@@ -398,10 +358,7 @@ less on for example extracting a probability distribution function (PDF). The PD
used in turn to make estimations and find causations such as given \( A \)
what is the likelihood of finding \( B \).
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec10">What is a good model? </h2>
<h2 id="___sec9">What is a good model? </h2>
<p>
In science and engineering we often end up in situations where we want to infer (or learn) a
@@ -428,10 +385,7 @@ for a given data set is an elusive, if not impossible, task. The fundamental dif
is that if we are not specific about what we mean by a <em>correct</em> model, there
could easily be many different models that fit the given data set <em>equally well</em>.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec11">What is a good model? Can we define it? </h2>
<h2 id="___sec10">What is a good model? Can we define it? </h2>
<p>
The central question is this: what leads us to say that a model is correct or
@@ -457,15 +411,7 @@ may first try the simplest class of models, namely linear models, followed obvio
<p>
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.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec12">Practicalities, choice of programming language and other computational issues </h2>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec13">Choice of Programming Language </h2>
<h2 id="___sec11">Choice of Programming Language </h2>
<p>
Python plays nowadays a central role in the development of machine
@@ -481,10 +427,7 @@ as <b>Scikit-Learn</b>, <b>Tensorflow</b> and <b>Pytorch</b>, but also on develo
algorithms and codes, we will as far as possible present many of these
algorithms either as a Python codes or C++ or Fortran (or other languages) codes.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec14">Data handling, machine learning and ethical aspects </h2>
<h2 id="___sec12">Data handling, machine learning and ethical aspects </h2>
<p>
In most of the cases we will study, we will either generate the data