lars/FYS-STK4155
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

update dot files

Browse Source
This commit is contained in:
mhjensen
2020-11-26 06:44:43 +01:00
parent 92d1df2030
commit daa7f2fd86
3 changed files with 935 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/pub/week48/html/._week48-bs061.html
+318
View File
@@ -0,0 +1,318 @@
<!--
Automatically generated HTML file from DocOnce source
(https://github.com/hplgit/doconce/)
-->
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="DocOnce: https://github.com/hplgit/doconce/" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<meta name="description" content="Week 48: Support Vector Machines and Summary of course">
<title>Week 48: Support Vector Machines and Summary of course</title>
<!-- Bootstrap style: bootstrap -->
<link href="https://netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css" rel="stylesheet">
<!-- not necessary
<link href="https://netdna.bootstrapcdn.com/font-awesome/4.0.3/css/font-awesome.css" rel="stylesheet">
-->
<style type="text/css">
/* Add scrollbar to dropdown menus in bootstrap navigation bar */
.dropdown-menu {
height: auto;
max-height: 400px;
overflow-x: hidden;
}
/* Adds an invisible element before each target to offset for the navigation
bar */
.anchor::before {
content:"";
display:block;
height:50px; /* fixed header height for style bootstrap */
margin:-50px 0 0; /* negative fixed header height */
}
</style>
</head>
<!-- tocinfo
{'highest level': 2,
'sections': [('Overview of week 48', 2, None, '___sec0'),
('Thursday', 2, None, '___sec1'),
('Friday', 2, None, '___sec2'),
('Support Vector Machines, overarching aims', 2, None, '___sec3'),
('Kernels and non-linearity', 2, None, '___sec4'),
('The equations', 2, None, '___sec5'),
('The problem to solve', 2, None, '___sec6'),
('Tailoring the equations to the usage of CVXOPT',
2,
None,
'___sec7'),
("Different kernels and Mercer's theorem", 2, None, '___sec8'),
('The moons example ("Adapted from Geron, chapter '
'5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")',
2,
None,
'___sec9'),
('Mathematical optimization of convex functions',
2,
None,
'___sec10'),
('How do we solve these problems?', 2, None, '___sec11'),
('A simple example', 2, None, '___sec12'),
('Back to the more realistic cases', 2, None, '___sec13'),
('Setting up the matrices and the problem', 2, None, '___sec14'),
('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq '
'\\boldsymbol{h}$',
2,
None,
'___sec15'),
('Summary of course', 2, None, '___sec16'),
('What? Me worry? No final exam in this course!',
2,
None,
'___sec17'),
('Topics we have covered this year', 2, None, '___sec18'),
('Statistical analysis and optimization of data',
2,
None,
'___sec19'),
('Machine learning', 2, None, '___sec20'),
('Learning outcomes and overarching aims of this course',
2,
None,
'___sec21'),
('Perspective on Machine Learning', 2, None, '___sec22'),
('Machine Learning Research', 2, None, '___sec23'),
('Starting your Machine Learning Project', 2, None, '___sec24'),
('Choose a Model and Algorithm', 2, None, '___sec25'),
('Preparing Your Data', 2, None, '___sec26'),
('Which Activation and Weights to Choose in Neural Networks',
2,
None,
'___sec27'),
('Optimization Methods and Hyperparameters', 2, None, '___sec28'),
('Resampling', 2, None, '___sec29'),
('Other courses on Data science and Machine Learning at UiO',
2,
None,
'___sec30'),
('Additional courses of interest', 2, None, '___sec31'),
("What's the future like?", 2, None, '___sec32'),
('Types of Machine Learning, a repetition', 2, None, '___sec33'),
('Why Boltzmann machines?', 2, None, '___sec34'),
('Boltzmann Machines', 2, None, '___sec35'),
('Some similarities and differences from DNNs',
2,
None,
'___sec36'),
('Boltzmann machines (BM)', 2, None, '___sec37'),
('A standard BM setup', 2, None, '___sec38'),
('The structure of the RBM network', 2, None, '___sec39'),
('The network', 2, None, '___sec40'),
('Goals', 2, None, '___sec41'),
('Joint distribution', 2, None, '___sec42'),
('Network Elements, the energy function', 2, None, '___sec43'),
('Defining different types of RBMs', 2, None, '___sec44'),
('More about RBMs', 2, None, '___sec45'),
('Autoencoders: Overarching view', 2, None, '___sec46'),
('Bayesian Machine Learning', 2, None, '___sec47'),
('Reinforcement Learning', 2, None, '___sec48'),
('Transfer learning', 2, None, '___sec49'),
('Adversarial learning', 2, None, '___sec50'),
('Dual learning', 2, None, '___sec51'),
('Distributed machine learning', 2, None, '___sec52'),
('Meta learning', 2, None, '___sec53'),
('The Challenges Facing Machine Learning', 2, None, '___sec54'),
('Explainable machine learning', 2, None, '___sec55'),
('Quantum machine learning', 2, None, '___sec56'),
('Quantum machine learning algorithms based on linear algebra',
2,
None,
'___sec57'),
('Quantum reinforcement learning', 2, None, '___sec58'),
('Quantum deep learning', 2, None, '___sec59'),
('Social machine learning', 2, None, '___sec60'),
('The last words?', 2, None, '___sec61'),
('Best wishes to you all and thanks so much for your heroic '
'efforts this semester',
2,
None,
'___sec62')]}
end of tocinfo -->
<body>
<script type="text/x-mathjax-config">
MathJax.Hub.Config({
TeX: {
equationNumbers: { autoNumber: "none" },
extensions: ["AMSmath.js", "AMSsymbols.js", "autobold.js", "color.js"]
}
});
</script>
<script type="text/javascript" async
src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML">
</script>
<!-- Bootstrap navigation bar -->
<div class="navbar navbar-default navbar-fixed-top">
<div class="navbar-header">
<button type="button" class="navbar-toggle" data-toggle="collapse" data-target=".navbar-responsive-collapse">
<span class="icon-bar"></span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<a class="navbar-brand" href="week48-bs.html">Week 48: Support Vector Machines and Summary of course</a>
</div>
<div class="navbar-collapse collapse navbar-responsive-collapse">
<ul class="nav navbar-nav navbar-right">
<li class="dropdown">
<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
<ul class="dropdown-menu">
<!-- navigation toc: --> <li><a href="._week48-bs001.html#___sec0" style="font-size: 80%;">Overview of week 48</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs002.html#___sec1" style="font-size: 80%;">Thursday</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs003.html#___sec2" style="font-size: 80%;">Friday</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs004.html#___sec3" style="font-size: 80%;">Support Vector Machines, overarching aims</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs005.html#___sec4" style="font-size: 80%;">Kernels and non-linearity</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs006.html#___sec5" style="font-size: 80%;">The equations</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs007.html#___sec6" style="font-size: 80%;">The problem to solve</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs008.html#___sec7" style="font-size: 80%;">Tailoring the equations to the usage of CVXOPT</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs009.html#___sec8" style="font-size: 80%;">Different kernels and Mercer's theorem</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs010.html#___sec9" style="font-size: 80%;">The moons example ("Adapted from Geron, chapter 5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs011.html#___sec10" style="font-size: 80%;">Mathematical optimization of convex functions</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs012.html#___sec11" style="font-size: 80%;">How do we solve these problems?</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs013.html#___sec12" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs014.html#___sec13" style="font-size: 80%;">Back to the more realistic cases</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs015.html#___sec14" style="font-size: 80%;">Setting up the matrices and the problem</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs016.html#___sec15" style="font-size: 80%;">Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs017.html#___sec16" style="font-size: 80%;">Summary of course</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs018.html#___sec17" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs019.html#___sec18" style="font-size: 80%;">Topics we have covered this year</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs020.html#___sec19" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs021.html#___sec20" style="font-size: 80%;">Machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs022.html#___sec21" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs023.html#___sec22" style="font-size: 80%;">Perspective on Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs024.html#___sec23" style="font-size: 80%;">Machine Learning Research</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs025.html#___sec24" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs026.html#___sec25" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs027.html#___sec26" style="font-size: 80%;">Preparing Your Data</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs028.html#___sec27" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs029.html#___sec28" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs030.html#___sec29" style="font-size: 80%;">Resampling</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs031.html#___sec30" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs032.html#___sec31" style="font-size: 80%;">Additional courses of interest</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs033.html#___sec32" style="font-size: 80%;">What's the future like?</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs034.html#___sec33" style="font-size: 80%;">Types of Machine Learning, a repetition</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs035.html#___sec34" style="font-size: 80%;">Why Boltzmann machines?</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs036.html#___sec35" style="font-size: 80%;">Boltzmann Machines</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs037.html#___sec36" style="font-size: 80%;">Some similarities and differences from DNNs</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs038.html#___sec37" style="font-size: 80%;">Boltzmann machines (BM)</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs039.html#___sec38" style="font-size: 80%;">A standard BM setup</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs040.html#___sec39" style="font-size: 80%;">The structure of the RBM network</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs041.html#___sec40" style="font-size: 80%;">The network</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs042.html#___sec41" style="font-size: 80%;">Goals</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs043.html#___sec42" style="font-size: 80%;">Joint distribution</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs044.html#___sec43" style="font-size: 80%;">Network Elements, the energy function</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs045.html#___sec44" style="font-size: 80%;">Defining different types of RBMs</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs046.html#___sec45" style="font-size: 80%;">More about RBMs</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs047.html#___sec46" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs048.html#___sec47" style="font-size: 80%;">Bayesian Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs049.html#___sec48" style="font-size: 80%;">Reinforcement Learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs050.html#___sec49" style="font-size: 80%;">Transfer learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs051.html#___sec50" style="font-size: 80%;">Adversarial learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs052.html#___sec51" style="font-size: 80%;">Dual learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs053.html#___sec52" style="font-size: 80%;">Distributed machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs054.html#___sec53" style="font-size: 80%;">Meta learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs055.html#___sec54" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs056.html#___sec55" style="font-size: 80%;">Explainable machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs057.html#___sec56" style="font-size: 80%;">Quantum machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs058.html#___sec57" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs059.html#___sec58" style="font-size: 80%;">Quantum reinforcement learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs060.html#___sec59" style="font-size: 80%;">Quantum deep learning</a></li>
<!-- navigation toc: --> <li><a href="#___sec60" style="font-size: 80%;">Social machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs062.html#___sec61" style="font-size: 80%;">The last words?</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs063.html#___sec62" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
</ul>
</li>
</ul>
</div>
</div>
</div> <!-- end of navigation bar -->
<div class="container">
<p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p> <!-- add vertical space -->
<a name="part0061"></a>
<!-- !split -->
<h2 id="___sec60" class="anchor">Social machine learning </h2>
<p>
Machine learning aims to imitate how humans
learn. While we have developed successful machine learning algorithms,
until now we have ignored one important fact: humans are social. Each
of us is one part of the total society and it is difficult for us to
live, learn, and improve ourselves, alone and isolated. Therefore, we
should design machines with social properties. Can we let machines
evolve by imitating human society so as to achieve more effective,
intelligent, interpretable &#8220;social machine learning&#8221;?
<p>
And much more.
<p>
<p>
<!-- navigation buttons at the bottom of the page -->
<ul class="pagination">
<li><a href="._week48-bs060.html">&laquo;</a></li>
<li><a href="._week48-bs000.html">1</a></li>
<li><a href="">...</a></li>
<li><a href="._week48-bs053.html">54</a></li>
<li><a href="._week48-bs054.html">55</a></li>
<li><a href="._week48-bs055.html">56</a></li>
<li><a href="._week48-bs056.html">57</a></li>
<li><a href="._week48-bs057.html">58</a></li>
<li><a href="._week48-bs058.html">59</a></li>
<li><a href="._week48-bs059.html">60</a></li>
<li><a href="._week48-bs060.html">61</a></li>
<li class="active"><a href="._week48-bs061.html">62</a></li>
<li><a href="._week48-bs062.html">63</a></li>
<li><a href="._week48-bs063.html">64</a></li>
<li><a href="._week48-bs062.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
</div> <!-- end container -->
<!-- include javascript, jQuery *first* -->
<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.10.2/jquery.min.js"></script>
<script src="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/js/bootstrap.min.js"></script>
<!-- Bootstrap footer
<footer>
<a href="http://..."><img width="250" align=right src="http://..."></a>
</footer>
-->
<center style="font-size:80%">
<!-- copyright only on the titlepage -->
</center>
</body>
</html>
doc/pub/week48/html/._week48-bs062.html
+311
View File
@@ -0,0 +1,311 @@
<!--
Automatically generated HTML file from DocOnce source
(https://github.com/hplgit/doconce/)
-->
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="DocOnce: https://github.com/hplgit/doconce/" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<meta name="description" content="Week 48: Support Vector Machines and Summary of course">
<title>Week 48: Support Vector Machines and Summary of course</title>
<!-- Bootstrap style: bootstrap -->
<link href="https://netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css" rel="stylesheet">
<!-- not necessary
<link href="https://netdna.bootstrapcdn.com/font-awesome/4.0.3/css/font-awesome.css" rel="stylesheet">
-->
<style type="text/css">
/* Add scrollbar to dropdown menus in bootstrap navigation bar */
.dropdown-menu {
height: auto;
max-height: 400px;
overflow-x: hidden;
}
/* Adds an invisible element before each target to offset for the navigation
bar */
.anchor::before {
content:"";
display:block;
height:50px; /* fixed header height for style bootstrap */
margin:-50px 0 0; /* negative fixed header height */
}
</style>
</head>
<!-- tocinfo
{'highest level': 2,
'sections': [('Overview of week 48', 2, None, '___sec0'),
('Thursday', 2, None, '___sec1'),
('Friday', 2, None, '___sec2'),
('Support Vector Machines, overarching aims', 2, None, '___sec3'),
('Kernels and non-linearity', 2, None, '___sec4'),
('The equations', 2, None, '___sec5'),
('The problem to solve', 2, None, '___sec6'),
('Tailoring the equations to the usage of CVXOPT',
2,
None,
'___sec7'),
("Different kernels and Mercer's theorem", 2, None, '___sec8'),
('The moons example ("Adapted from Geron, chapter '
'5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")',
2,
None,
'___sec9'),
('Mathematical optimization of convex functions',
2,
None,
'___sec10'),
('How do we solve these problems?', 2, None, '___sec11'),
('A simple example', 2, None, '___sec12'),
('Back to the more realistic cases', 2, None, '___sec13'),
('Setting up the matrices and the problem', 2, None, '___sec14'),
('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq '
'\\boldsymbol{h}$',
2,
None,
'___sec15'),
('Summary of course', 2, None, '___sec16'),
('What? Me worry? No final exam in this course!',
2,
None,
'___sec17'),
('Topics we have covered this year', 2, None, '___sec18'),
('Statistical analysis and optimization of data',
2,
None,
'___sec19'),
('Machine learning', 2, None, '___sec20'),
('Learning outcomes and overarching aims of this course',
2,
None,
'___sec21'),
('Perspective on Machine Learning', 2, None, '___sec22'),
('Machine Learning Research', 2, None, '___sec23'),
('Starting your Machine Learning Project', 2, None, '___sec24'),
('Choose a Model and Algorithm', 2, None, '___sec25'),
('Preparing Your Data', 2, None, '___sec26'),
('Which Activation and Weights to Choose in Neural Networks',
2,
None,
'___sec27'),
('Optimization Methods and Hyperparameters', 2, None, '___sec28'),
('Resampling', 2, None, '___sec29'),
('Other courses on Data science and Machine Learning at UiO',
2,
None,
'___sec30'),
('Additional courses of interest', 2, None, '___sec31'),
("What's the future like?", 2, None, '___sec32'),
('Types of Machine Learning, a repetition', 2, None, '___sec33'),
('Why Boltzmann machines?', 2, None, '___sec34'),
('Boltzmann Machines', 2, None, '___sec35'),
('Some similarities and differences from DNNs',
2,
None,
'___sec36'),
('Boltzmann machines (BM)', 2, None, '___sec37'),
('A standard BM setup', 2, None, '___sec38'),
('The structure of the RBM network', 2, None, '___sec39'),
('The network', 2, None, '___sec40'),
('Goals', 2, None, '___sec41'),
('Joint distribution', 2, None, '___sec42'),
('Network Elements, the energy function', 2, None, '___sec43'),
('Defining different types of RBMs', 2, None, '___sec44'),
('More about RBMs', 2, None, '___sec45'),
('Autoencoders: Overarching view', 2, None, '___sec46'),
('Bayesian Machine Learning', 2, None, '___sec47'),
('Reinforcement Learning', 2, None, '___sec48'),
('Transfer learning', 2, None, '___sec49'),
('Adversarial learning', 2, None, '___sec50'),
('Dual learning', 2, None, '___sec51'),
('Distributed machine learning', 2, None, '___sec52'),
('Meta learning', 2, None, '___sec53'),
('The Challenges Facing Machine Learning', 2, None, '___sec54'),
('Explainable machine learning', 2, None, '___sec55'),
('Quantum machine learning', 2, None, '___sec56'),
('Quantum machine learning algorithms based on linear algebra',
2,
None,
'___sec57'),
('Quantum reinforcement learning', 2, None, '___sec58'),
('Quantum deep learning', 2, None, '___sec59'),
('Social machine learning', 2, None, '___sec60'),
('The last words?', 2, None, '___sec61'),
('Best wishes to you all and thanks so much for your heroic '
'efforts this semester',
2,
None,
'___sec62')]}
end of tocinfo -->
<body>
<script type="text/x-mathjax-config">
MathJax.Hub.Config({
TeX: {
equationNumbers: { autoNumber: "none" },
extensions: ["AMSmath.js", "AMSsymbols.js", "autobold.js", "color.js"]
}
});
</script>
<script type="text/javascript" async
src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML">
</script>
<!-- Bootstrap navigation bar -->
<div class="navbar navbar-default navbar-fixed-top">
<div class="navbar-header">
<button type="button" class="navbar-toggle" data-toggle="collapse" data-target=".navbar-responsive-collapse">
<span class="icon-bar"></span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<a class="navbar-brand" href="week48-bs.html">Week 48: Support Vector Machines and Summary of course</a>
</div>
<div class="navbar-collapse collapse navbar-responsive-collapse">
<ul class="nav navbar-nav navbar-right">
<li class="dropdown">
<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
<ul class="dropdown-menu">
<!-- navigation toc: --> <li><a href="._week48-bs001.html#___sec0" style="font-size: 80%;">Overview of week 48</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs002.html#___sec1" style="font-size: 80%;">Thursday</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs003.html#___sec2" style="font-size: 80%;">Friday</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs004.html#___sec3" style="font-size: 80%;">Support Vector Machines, overarching aims</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs005.html#___sec4" style="font-size: 80%;">Kernels and non-linearity</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs006.html#___sec5" style="font-size: 80%;">The equations</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs007.html#___sec6" style="font-size: 80%;">The problem to solve</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs008.html#___sec7" style="font-size: 80%;">Tailoring the equations to the usage of CVXOPT</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs009.html#___sec8" style="font-size: 80%;">Different kernels and Mercer's theorem</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs010.html#___sec9" style="font-size: 80%;">The moons example ("Adapted from Geron, chapter 5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs011.html#___sec10" style="font-size: 80%;">Mathematical optimization of convex functions</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs012.html#___sec11" style="font-size: 80%;">How do we solve these problems?</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs013.html#___sec12" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs014.html#___sec13" style="font-size: 80%;">Back to the more realistic cases</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs015.html#___sec14" style="font-size: 80%;">Setting up the matrices and the problem</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs016.html#___sec15" style="font-size: 80%;">Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs017.html#___sec16" style="font-size: 80%;">Summary of course</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs018.html#___sec17" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs019.html#___sec18" style="font-size: 80%;">Topics we have covered this year</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs020.html#___sec19" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs021.html#___sec20" style="font-size: 80%;">Machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs022.html#___sec21" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs023.html#___sec22" style="font-size: 80%;">Perspective on Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs024.html#___sec23" style="font-size: 80%;">Machine Learning Research</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs025.html#___sec24" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs026.html#___sec25" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs027.html#___sec26" style="font-size: 80%;">Preparing Your Data</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs028.html#___sec27" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs029.html#___sec28" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs030.html#___sec29" style="font-size: 80%;">Resampling</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs031.html#___sec30" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs032.html#___sec31" style="font-size: 80%;">Additional courses of interest</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs033.html#___sec32" style="font-size: 80%;">What's the future like?</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs034.html#___sec33" style="font-size: 80%;">Types of Machine Learning, a repetition</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs035.html#___sec34" style="font-size: 80%;">Why Boltzmann machines?</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs036.html#___sec35" style="font-size: 80%;">Boltzmann Machines</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs037.html#___sec36" style="font-size: 80%;">Some similarities and differences from DNNs</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs038.html#___sec37" style="font-size: 80%;">Boltzmann machines (BM)</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs039.html#___sec38" style="font-size: 80%;">A standard BM setup</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs040.html#___sec39" style="font-size: 80%;">The structure of the RBM network</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs041.html#___sec40" style="font-size: 80%;">The network</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs042.html#___sec41" style="font-size: 80%;">Goals</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs043.html#___sec42" style="font-size: 80%;">Joint distribution</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs044.html#___sec43" style="font-size: 80%;">Network Elements, the energy function</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs045.html#___sec44" style="font-size: 80%;">Defining different types of RBMs</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs046.html#___sec45" style="font-size: 80%;">More about RBMs</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs047.html#___sec46" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs048.html#___sec47" style="font-size: 80%;">Bayesian Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs049.html#___sec48" style="font-size: 80%;">Reinforcement Learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs050.html#___sec49" style="font-size: 80%;">Transfer learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs051.html#___sec50" style="font-size: 80%;">Adversarial learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs052.html#___sec51" style="font-size: 80%;">Dual learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs053.html#___sec52" style="font-size: 80%;">Distributed machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs054.html#___sec53" style="font-size: 80%;">Meta learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs055.html#___sec54" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs056.html#___sec55" style="font-size: 80%;">Explainable machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs057.html#___sec56" style="font-size: 80%;">Quantum machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs058.html#___sec57" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs059.html#___sec58" style="font-size: 80%;">Quantum reinforcement learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs060.html#___sec59" style="font-size: 80%;">Quantum deep learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs061.html#___sec60" style="font-size: 80%;">Social machine learning</a></li>
<!-- navigation toc: --> <li><a href="#___sec61" style="font-size: 80%;">The last words?</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs063.html#___sec62" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
</ul>
</li>
</ul>
</div>
</div>
</div> <!-- end of navigation bar -->
<div class="container">
<p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p> <!-- add vertical space -->
<a name="part0062"></a>
<!-- !split -->
<h2 id="___sec61" class="anchor">The last words? </h2>
<p>
Early computer scientist Alan Kay said, <b>The best way to predict the
future is to create it</b>. Therefore, all machine learning
practitioners, whether scholars or engineers, professors or students,
need to work together to advance these important research
topics. Together, we will not just predict the future, but create it.
<p>
<p>
<!-- navigation buttons at the bottom of the page -->
<ul class="pagination">
<li><a href="._week48-bs061.html">&laquo;</a></li>
<li><a href="._week48-bs000.html">1</a></li>
<li><a href="">...</a></li>
<li><a href="._week48-bs054.html">55</a></li>
<li><a href="._week48-bs055.html">56</a></li>
<li><a href="._week48-bs056.html">57</a></li>
<li><a href="._week48-bs057.html">58</a></li>
<li><a href="._week48-bs058.html">59</a></li>
<li><a href="._week48-bs059.html">60</a></li>
<li><a href="._week48-bs060.html">61</a></li>
<li><a href="._week48-bs061.html">62</a></li>
<li class="active"><a href="._week48-bs062.html">63</a></li>
<li><a href="._week48-bs063.html">64</a></li>
<li><a href="._week48-bs063.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
</div> <!-- end container -->
<!-- include javascript, jQuery *first* -->
<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.10.2/jquery.min.js"></script>
<script src="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/js/bootstrap.min.js"></script>
<!-- Bootstrap footer
<footer>
<a href="http://..."><img width="250" align=right src="http://..."></a>
</footer>
-->
<center style="font-size:80%">
<!-- copyright only on the titlepage -->
</center>
</body>
</html>
doc/pub/week48/html/._week48-bs063.html
+306
View File
@@ -0,0 +1,306 @@
<!--
Automatically generated HTML file from DocOnce source
(https://github.com/hplgit/doconce/)
-->
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="DocOnce: https://github.com/hplgit/doconce/" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<meta name="description" content="Week 48: Support Vector Machines and Summary of course">
<title>Week 48: Support Vector Machines and Summary of course</title>
<!-- Bootstrap style: bootstrap -->
<link href="https://netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css" rel="stylesheet">
<!-- not necessary
<link href="https://netdna.bootstrapcdn.com/font-awesome/4.0.3/css/font-awesome.css" rel="stylesheet">
-->
<style type="text/css">
/* Add scrollbar to dropdown menus in bootstrap navigation bar */
.dropdown-menu {
height: auto;
max-height: 400px;
overflow-x: hidden;
}
/* Adds an invisible element before each target to offset for the navigation
bar */
.anchor::before {
content:"";
display:block;
height:50px; /* fixed header height for style bootstrap */
margin:-50px 0 0; /* negative fixed header height */
}
</style>
</head>
<!-- tocinfo
{'highest level': 2,
'sections': [('Overview of week 48', 2, None, '___sec0'),
('Thursday', 2, None, '___sec1'),
('Friday', 2, None, '___sec2'),
('Support Vector Machines, overarching aims', 2, None, '___sec3'),
('Kernels and non-linearity', 2, None, '___sec4'),
('The equations', 2, None, '___sec5'),
('The problem to solve', 2, None, '___sec6'),
('Tailoring the equations to the usage of CVXOPT',
2,
None,
'___sec7'),
("Different kernels and Mercer's theorem", 2, None, '___sec8'),
('The moons example ("Adapted from Geron, chapter '
'5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")',
2,
None,
'___sec9'),
('Mathematical optimization of convex functions',
2,
None,
'___sec10'),
('How do we solve these problems?', 2, None, '___sec11'),
('A simple example', 2, None, '___sec12'),
('Back to the more realistic cases', 2, None, '___sec13'),
('Setting up the matrices and the problem', 2, None, '___sec14'),
('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq '
'\\boldsymbol{h}$',
2,
None,
'___sec15'),
('Summary of course', 2, None, '___sec16'),
('What? Me worry? No final exam in this course!',
2,
None,
'___sec17'),
('Topics we have covered this year', 2, None, '___sec18'),
('Statistical analysis and optimization of data',
2,
None,
'___sec19'),
('Machine learning', 2, None, '___sec20'),
('Learning outcomes and overarching aims of this course',
2,
None,
'___sec21'),
('Perspective on Machine Learning', 2, None, '___sec22'),
('Machine Learning Research', 2, None, '___sec23'),
('Starting your Machine Learning Project', 2, None, '___sec24'),
('Choose a Model and Algorithm', 2, None, '___sec25'),
('Preparing Your Data', 2, None, '___sec26'),
('Which Activation and Weights to Choose in Neural Networks',
2,
None,
'___sec27'),
('Optimization Methods and Hyperparameters', 2, None, '___sec28'),
('Resampling', 2, None, '___sec29'),
('Other courses on Data science and Machine Learning at UiO',
2,
None,
'___sec30'),
('Additional courses of interest', 2, None, '___sec31'),
("What's the future like?", 2, None, '___sec32'),
('Types of Machine Learning, a repetition', 2, None, '___sec33'),
('Why Boltzmann machines?', 2, None, '___sec34'),
('Boltzmann Machines', 2, None, '___sec35'),
('Some similarities and differences from DNNs',
2,
None,
'___sec36'),
('Boltzmann machines (BM)', 2, None, '___sec37'),
('A standard BM setup', 2, None, '___sec38'),
('The structure of the RBM network', 2, None, '___sec39'),
('The network', 2, None, '___sec40'),
('Goals', 2, None, '___sec41'),
('Joint distribution', 2, None, '___sec42'),
('Network Elements, the energy function', 2, None, '___sec43'),
('Defining different types of RBMs', 2, None, '___sec44'),
('More about RBMs', 2, None, '___sec45'),
('Autoencoders: Overarching view', 2, None, '___sec46'),
('Bayesian Machine Learning', 2, None, '___sec47'),
('Reinforcement Learning', 2, None, '___sec48'),
('Transfer learning', 2, None, '___sec49'),
('Adversarial learning', 2, None, '___sec50'),
('Dual learning', 2, None, '___sec51'),
('Distributed machine learning', 2, None, '___sec52'),
('Meta learning', 2, None, '___sec53'),
('The Challenges Facing Machine Learning', 2, None, '___sec54'),
('Explainable machine learning', 2, None, '___sec55'),
('Quantum machine learning', 2, None, '___sec56'),
('Quantum machine learning algorithms based on linear algebra',
2,
None,
'___sec57'),
('Quantum reinforcement learning', 2, None, '___sec58'),
('Quantum deep learning', 2, None, '___sec59'),
('Social machine learning', 2, None, '___sec60'),
('The last words?', 2, None, '___sec61'),
('Best wishes to you all and thanks so much for your heroic '
'efforts this semester',
2,
None,
'___sec62')]}
end of tocinfo -->
<body>
<script type="text/x-mathjax-config">
MathJax.Hub.Config({
TeX: {
equationNumbers: { autoNumber: "none" },
extensions: ["AMSmath.js", "AMSsymbols.js", "autobold.js", "color.js"]
}
});
</script>
<script type="text/javascript" async
src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML">
</script>
<!-- Bootstrap navigation bar -->
<div class="navbar navbar-default navbar-fixed-top">
<div class="navbar-header">
<button type="button" class="navbar-toggle" data-toggle="collapse" data-target=".navbar-responsive-collapse">
<span class="icon-bar"></span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<a class="navbar-brand" href="week48-bs.html">Week 48: Support Vector Machines and Summary of course</a>
</div>
<div class="navbar-collapse collapse navbar-responsive-collapse">
<ul class="nav navbar-nav navbar-right">
<li class="dropdown">
<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
<ul class="dropdown-menu">
<!-- navigation toc: --> <li><a href="._week48-bs001.html#___sec0" style="font-size: 80%;">Overview of week 48</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs002.html#___sec1" style="font-size: 80%;">Thursday</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs003.html#___sec2" style="font-size: 80%;">Friday</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs004.html#___sec3" style="font-size: 80%;">Support Vector Machines, overarching aims</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs005.html#___sec4" style="font-size: 80%;">Kernels and non-linearity</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs006.html#___sec5" style="font-size: 80%;">The equations</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs007.html#___sec6" style="font-size: 80%;">The problem to solve</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs008.html#___sec7" style="font-size: 80%;">Tailoring the equations to the usage of CVXOPT</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs009.html#___sec8" style="font-size: 80%;">Different kernels and Mercer's theorem</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs010.html#___sec9" style="font-size: 80%;">The moons example ("Adapted from Geron, chapter 5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs011.html#___sec10" style="font-size: 80%;">Mathematical optimization of convex functions</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs012.html#___sec11" style="font-size: 80%;">How do we solve these problems?</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs013.html#___sec12" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs014.html#___sec13" style="font-size: 80%;">Back to the more realistic cases</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs015.html#___sec14" style="font-size: 80%;">Setting up the matrices and the problem</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs016.html#___sec15" style="font-size: 80%;">Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs017.html#___sec16" style="font-size: 80%;">Summary of course</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs018.html#___sec17" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs019.html#___sec18" style="font-size: 80%;">Topics we have covered this year</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs020.html#___sec19" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs021.html#___sec20" style="font-size: 80%;">Machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs022.html#___sec21" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs023.html#___sec22" style="font-size: 80%;">Perspective on Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs024.html#___sec23" style="font-size: 80%;">Machine Learning Research</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs025.html#___sec24" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs026.html#___sec25" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs027.html#___sec26" style="font-size: 80%;">Preparing Your Data</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs028.html#___sec27" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs029.html#___sec28" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs030.html#___sec29" style="font-size: 80%;">Resampling</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs031.html#___sec30" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs032.html#___sec31" style="font-size: 80%;">Additional courses of interest</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs033.html#___sec32" style="font-size: 80%;">What's the future like?</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs034.html#___sec33" style="font-size: 80%;">Types of Machine Learning, a repetition</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs035.html#___sec34" style="font-size: 80%;">Why Boltzmann machines?</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs036.html#___sec35" style="font-size: 80%;">Boltzmann Machines</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs037.html#___sec36" style="font-size: 80%;">Some similarities and differences from DNNs</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs038.html#___sec37" style="font-size: 80%;">Boltzmann machines (BM)</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs039.html#___sec38" style="font-size: 80%;">A standard BM setup</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs040.html#___sec39" style="font-size: 80%;">The structure of the RBM network</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs041.html#___sec40" style="font-size: 80%;">The network</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs042.html#___sec41" style="font-size: 80%;">Goals</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs043.html#___sec42" style="font-size: 80%;">Joint distribution</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs044.html#___sec43" style="font-size: 80%;">Network Elements, the energy function</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs045.html#___sec44" style="font-size: 80%;">Defining different types of RBMs</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs046.html#___sec45" style="font-size: 80%;">More about RBMs</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs047.html#___sec46" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs048.html#___sec47" style="font-size: 80%;">Bayesian Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs049.html#___sec48" style="font-size: 80%;">Reinforcement Learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs050.html#___sec49" style="font-size: 80%;">Transfer learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs051.html#___sec50" style="font-size: 80%;">Adversarial learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs052.html#___sec51" style="font-size: 80%;">Dual learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs053.html#___sec52" style="font-size: 80%;">Distributed machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs054.html#___sec53" style="font-size: 80%;">Meta learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs055.html#___sec54" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs056.html#___sec55" style="font-size: 80%;">Explainable machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs057.html#___sec56" style="font-size: 80%;">Quantum machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs058.html#___sec57" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs059.html#___sec58" style="font-size: 80%;">Quantum reinforcement learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs060.html#___sec59" style="font-size: 80%;">Quantum deep learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs061.html#___sec60" style="font-size: 80%;">Social machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs062.html#___sec61" style="font-size: 80%;">The last words?</a></li>
<!-- navigation toc: --> <li><a href="#___sec62" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
</ul>
</li>
</ul>
</div>
</div>
</div> <!-- end of navigation bar -->
<div class="container">
<p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p> <!-- add vertical space -->
<a name="part0063"></a>
<!-- !split -->
<h2 id="___sec62" class="anchor">Best wishes to you all and thanks so much for your heroic efforts this semester </h2>
<p>
<br /><br /><center><p><img src="figures/Nebbdyr2.png" align="bottom" width=500></p></center><br /><br />
<p>
<p>
<!-- navigation buttons at the bottom of the page -->
<ul class="pagination">
<li><a href="._week48-bs062.html">&laquo;</a></li>
<li><a href="._week48-bs000.html">1</a></li>
<li><a href="">...</a></li>
<li><a href="._week48-bs055.html">56</a></li>
<li><a href="._week48-bs056.html">57</a></li>
<li><a href="._week48-bs057.html">58</a></li>
<li><a href="._week48-bs058.html">59</a></li>
<li><a href="._week48-bs059.html">60</a></li>
<li><a href="._week48-bs060.html">61</a></li>
<li><a href="._week48-bs061.html">62</a></li>
<li><a href="._week48-bs062.html">63</a></li>
<li class="active"><a href="._week48-bs063.html">64</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
</div> <!-- end container -->
<!-- include javascript, jQuery *first* -->
<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.10.2/jquery.min.js"></script>
<script src="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/js/bootstrap.min.js"></script>
<!-- Bootstrap footer
<footer>
<a href="http://..."><img width="250" align=right src="http://..."></a>
</footer>
-->
<center style="font-size:80%">
<!-- copyright only on the titlepage -->
</center>
</body>
</html>