Files related to Jeff Fessler's Machine Learning tutorial presentations
at these conferences:
<ul>
<li>
2018 ISMRM Workshop on Machine Learning 2
<a href="talk.pdf">
(pdf file of slides)</a>
<li>
2021 ISMRM Educational Course:
Machine Learning: Everything You Wanted to Know but Were Afraid to Ask
<a href="slides-ismrm-21.pdf">
(pdf file of slides)</a>
</ul>

<p>
"Basic introduction to machine learning"

<p>
Updated versions of these examples
are at
<a href="https://github.com/JeffFessler/ismrm_ml2">
https://github.com/JeffFessler/ismrm_ml2</a>

<p>
The full URL for this directory is
<br>
<a href="http://web.eecs.umich.edu/~fessler/papers/files/talk/18/ml2jf/">
http://web.eecs.umich.edu/~fessler/papers/files/talk/18/ml2jf/
</a>
<br>
and a short URL is
<br>
<a href="https://tinyurl.com/ml2-18-jf">
https://tinyurl.com/ml2-18-jf
</a>

<p>
There are 3 Jupyter notebooks
that are self-contained
(assuming you have installed Julia and a few standard packages).
There are also HTML versions
of those notebooks
that you can view in any browser
without installing software.

<p>
Contents:
<ul>

<li>
<a href="01-overview.html">
01-overview.html
</a>
(Basic figures giving big picture about machine learning)

<li>
<a href="01-overview.ipynb">
01-overview.ipynb
</a>


<li>
<a href="02-digits.html">
02-digits.html
</a>
(Figures related to handwritten digit classification;
this one is a work-in-progress.)

<li>
<a href="02-digits.ipynb">
02-digits.ipynb
</a>


<li>
<a href="03-flux-ring2.html">
03-flux-ring2.html
</a>
(Simple NN training example for binary classification)

<li>
<a href="03-flux-ring2.ipynb">
03-flux-ring2.ipynb
</a>


<li>
<a href="04-denoise-1d.html">
04-denoise-1d.html
</a>
(NN based denoising of 1D signals)

<li>
<a href="04-denoise-1d.ipynb">
04-denoise-1d.ipynb
</a>

</ul>