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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-bs.html
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@@ -43,29 +43,23 @@ Automatically generated HTML file from DocOnce source
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'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 '
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None,
'___sec12'),
('Choice of Programming Language', 2, None, '___sec13'),
'___sec2'),
('Machine Learning, an extremely rich field', 2, None, '___sec3'),
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('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',
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'___sec14')]}
'___sec12')]}
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<body>
@@ -75,7 +69,7 @@ end of tocinfo -->
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@@ -105,19 +99,17 @@ MathJax.Hub.Config({
<ul class="dropdown-menu">
<!-- navigation toc: --> <li><a href="#___sec0" style="font-size: 80%;">Introduction</a></li>
<!-- navigation toc: --> <li><a href="#___sec1" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="#___sec2" style="font-size: 80%;">Machine Learning, short overview</a></li>
<!-- navigation toc: --> <li><a href="#___sec3" style="font-size: 80%;">Machine Learning, a small (and probably biased) introduction</a></li>
<!-- navigation toc: --> <li><a href="#___sec4" style="font-size: 80%;">Machine Learning, an extremely rich field</a></li>
<!-- navigation toc: --> <li><a href="#___sec5" style="font-size: 80%;">A multidisciplinary approach</a></li>
<!-- navigation toc: --> <li><a href="#___sec6" style="font-size: 80%;">Types of Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="#___sec7" style="font-size: 80%;">Essential elements of ML</a></li>
<!-- navigation toc: --> <li><a href="#___sec8" style="font-size: 80%;">An optimization/minimization problem</a></li>
<!-- navigation toc: --> <li><a href="#___sec9" style="font-size: 80%;">A Frequentist approach to data analysis</a></li>
<!-- navigation toc: --> <li><a href="#___sec10" style="font-size: 80%;">What is a good model?</a></li>
<!-- navigation toc: --> <li><a href="#___sec11" style="font-size: 80%;">What is a good model? Can we define it?</a></li>
<!-- navigation toc: --> <li><a href="#___sec12" style="font-size: 80%;">Practicalities, choice of programming language and other computational issues</a></li>
<!-- navigation toc: --> <li><a href="#___sec13" style="font-size: 80%;">Choice of Programming Language</a></li>
<!-- navigation toc: --> <li><a href="#___sec14" style="font-size: 80%;">Data handling, machine learning and ethical aspects</a></li>
<!-- navigation toc: --> <li><a href="#___sec2" style="font-size: 80%;">Machine Learning, a small (and probably biased) introduction</a></li>
<!-- navigation toc: --> <li><a href="#___sec3" style="font-size: 80%;">Machine Learning, an extremely rich field</a></li>
<!-- navigation toc: --> <li><a href="#___sec4" style="font-size: 80%;">A multidisciplinary approach</a></li>
<!-- navigation toc: --> <li><a href="#___sec5" style="font-size: 80%;">Types of Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="#___sec6" style="font-size: 80%;">Essential elements of ML</a></li>
<!-- navigation toc: --> <li><a href="#___sec7" style="font-size: 80%;">An optimization/minimization problem</a></li>
<!-- navigation toc: --> <li><a href="#___sec8" style="font-size: 80%;">A Frequentist approach to data analysis</a></li>
<!-- navigation toc: --> <li><a href="#___sec9" style="font-size: 80%;">What is a good model?</a></li>
<!-- navigation toc: --> <li><a href="#___sec10" style="font-size: 80%;">What is a good model? Can we define it?</a></li>
<!-- navigation toc: --> <li><a href="#___sec11" style="font-size: 80%;">Choice of Programming Language</a></li>
<!-- navigation toc: --> <li><a href="#___sec12" style="font-size: 80%;">Data handling, machine learning and ethical aspects</a></li>
</ul>
</li>
@@ -130,6 +122,7 @@ MathJax.Hub.Config({
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@@ -151,14 +144,11 @@ 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>
<!-- potential-jumbotron-button -->
</div> <!-- end jumbotron -->
<!-- !split -->
<h2 id="___sec0" class="anchor">Introduction </h2>
<p>
@@ -240,9 +230,6 @@ 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" class="anchor">Learning outcomes </h2>
<p>
@@ -297,18 +284,7 @@ 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 -->
<p>
<!-- !split -->
<h2 id="___sec2" class="anchor">Machine Learning, short overview </h2>
<p>
<!-- !split -->
<h2 id="___sec3" class="anchor">Machine Learning, a small (and probably biased) introduction </h2>
<h2 id="___sec2" class="anchor">Machine Learning, a small (and probably biased) introduction </h2>
<p>
Ideally, machine learning represents the science of giving computers
@@ -321,10 +297,7 @@ 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 -->
<h2 id="___sec4" class="anchor">Machine Learning, an extremely rich field </h2>
<h2 id="___sec3" class="anchor">Machine Learning, an extremely rich field </h2>
<p>
Machine learning is an extremely rich field, in spite of its young
@@ -341,10 +314,7 @@ 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 -->
<h2 id="___sec5" class="anchor">A multidisciplinary approach </h2>
<h2 id="___sec4" class="anchor">A multidisciplinary approach </h2>
<p>
Not all the
@@ -356,10 +326,7 @@ 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 -->
<h2 id="___sec6" class="anchor">Types of Machine Learning </h2>
<h2 id="___sec5" class="anchor">Types of Machine Learning </h2>
<p>
The approaches to machine learning are many, but are often split into
@@ -387,10 +354,7 @@ desired output of a system. Some of the most common tasks are:
<!-- !epop -->
<p>
<!-- !split -->
<h2 id="___sec7" class="anchor">Essential elements of ML </h2>
<h2 id="___sec6" class="anchor">Essential elements of ML </h2>
<p>
The methods we cover have three main topics in common, irrespective of
@@ -405,18 +369,12 @@ whether we deal with supervised or unsupervised learning.
<!-- !epop -->
<p>
<!-- !split -->
<h2 id="___sec8" class="anchor">An optimization/minimization problem </h2>
<h2 id="___sec7" class="anchor">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 -->
<h2 id="___sec9" class="anchor">A Frequentist approach to data analysis </h2>
<h2 id="___sec8" class="anchor">A Frequentist approach to data analysis </h2>
<p>
When you hear phrases like <b>predictions and estimations</b> and
@@ -446,10 +404,7 @@ 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 -->
<h2 id="___sec10" class="anchor">What is a good model? </h2>
<h2 id="___sec9" class="anchor">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
@@ -476,10 +431,7 @@ 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 -->
<h2 id="___sec11" class="anchor">What is a good model? Can we define it? </h2>
<h2 id="___sec10" class="anchor">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
@@ -505,15 +457,7 @@ 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 -->
<h2 id="___sec12" class="anchor">Practicalities, choice of programming language and other computational issues </h2>
<p>
<!-- !split -->
<h2 id="___sec13" class="anchor">Choice of Programming Language </h2>
<h2 id="___sec11" class="anchor">Choice of Programming Language </h2>
<p>
Python plays nowadays a central role in the development of machine
@@ -529,10 +473,7 @@ 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 -->
<h2 id="___sec14" class="anchor">Data handling, machine learning and ethical aspects </h2>
<h2 id="___sec12" class="anchor">Data handling, machine learning and ethical aspects </h2>
<p>
In most of the cases we will study, we will either generate the data
@@ -634,6 +575,11 @@ society.
<p>
<p>
<!-- navigation buttons at the bottom of the page -->
<ul class="pagination">
<li class="active"><a href="._Introduction-bs000.html">1</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
</div> <!-- end container -->