mhjensen
249 lines
12 KiB
HTML
249 lines
12 KiB
HTML
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<title>Summary of course</title>
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'sections': [('What? Me worry? No final exam in this course!',
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2,
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('What did I learn in school this year?', 2, None, '___sec1'),
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('Statistical analysis and optimization of data',
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('Perspective on Machine Learning', 2, None, '___sec6'),
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('Starting your Machine Learning Project', 2, None, '___sec8'),
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('Choose a Model and Algorithm', 2, None, '___sec9'),
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('Preparing Your Data', 2, None, '___sec10'),
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('Additional courses of interest', 2, None, '___sec15'),
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("What's the future like?", 2, None, '___sec16'),
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('Bayesian Machine Learning', 2, None, '___sec17'),
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('Reinforcement Learning', 2, None, '___sec18'),
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('Distributed machine learning', 2, None, '___sec22'),
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('Meta learning', 2, None, '___sec23'),
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('The Challenges Facing Machine Learning', 2, None, '___sec24'),
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('Explainable machine learning', 2, None, '___sec25'),
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('Quantum machine learning', 2, None, '___sec26'),
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('Quantum machine learning algorithms based on linear algebra',
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2,
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('Quantum reinforcement learning', 2, None, '___sec28'),
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('Quantum deep learning', 2, None, '___sec29'),
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('The last words?', 2, None, '___sec31'),
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('Best wishes to you all and thanks so much for your heroic '
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<a class="navbar-brand" href="summary-bs.html">Summary of course</a>
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<li class="dropdown">
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<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
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<ul class="dropdown-menu">
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<!-- navigation toc: --> <li><a href="._summary-bs001.html#___sec0" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs002.html#___sec1" style="font-size: 80%;">What did I learn in school this year?</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs003.html#___sec2" style="font-size: 80%;">Topics we have covered this year</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs004.html#___sec3" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs005.html#___sec4" style="font-size: 80%;">Machine learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs007.html#___sec6" style="font-size: 80%;">Perspective on Machine Learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs008.html#___sec7" style="font-size: 80%;">Machine Learning Research</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs009.html#___sec8" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs010.html#___sec9" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs011.html#___sec10" style="font-size: 80%;">Preparing Your Data</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs012.html#___sec11" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs013.html#___sec12" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs014.html#___sec13" style="font-size: 80%;">Resampling</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs015.html#___sec14" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs016.html#___sec15" style="font-size: 80%;">Additional courses of interest</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs017.html#___sec16" style="font-size: 80%;">What's the future like?</a></li>
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<!-- navigation toc: --> <li><a href="#___sec17" style="font-size: 80%;">Bayesian Machine Learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs019.html#___sec18" style="font-size: 80%;">Reinforcement Learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs020.html#___sec19" style="font-size: 80%;">Transfer learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs021.html#___sec20" style="font-size: 80%;">Adversarial learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs022.html#___sec21" style="font-size: 80%;">Dual learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs023.html#___sec22" style="font-size: 80%;">Distributed machine learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs024.html#___sec23" style="font-size: 80%;">Meta learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs025.html#___sec24" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs026.html#___sec25" style="font-size: 80%;">Explainable machine learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs027.html#___sec26" style="font-size: 80%;">Quantum machine learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs028.html#___sec27" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs029.html#___sec28" style="font-size: 80%;">Quantum reinforcement learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs030.html#___sec29" style="font-size: 80%;">Quantum deep learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs031.html#___sec30" style="font-size: 80%;">Social machine learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs032.html#___sec31" style="font-size: 80%;">The last words?</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs033.html#___sec32" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
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</ul>
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</li>
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</ul>
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<div class="container">
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<p> </p><p> </p><p> </p> <!-- add vertical space -->
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<a name="part0018"></a>
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<!-- !split -->
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<h2 id="___sec17" class="anchor">Bayesian Machine Learning </h2>
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<p>
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This is an important topic if we aim at extracting a probability
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distribution. This gives us also a confidence interval and error
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estimates.
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<p>
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Bayesian machine learning allows us to encode our prior beliefs about
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what those models should look like, independent of what the data tells
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us. This is especially useful when we don’t have a ton of data to
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confidently learn our model.
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<p>
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<p>
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<li><a href="._summary-bs017.html">«</a></li>
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<li><a href="._summary-bs000.html">1</a></li>
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<li><a href="">...</a></li>
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<li><a href="._summary-bs010.html">11</a></li>
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<li><a href="._summary-bs011.html">12</a></li>
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<li><a href="._summary-bs012.html">13</a></li>
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<li><a href="._summary-bs013.html">14</a></li>
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<li><a href="._summary-bs014.html">15</a></li>
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<li><a href="._summary-bs015.html">16</a></li>
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<li><a href="._summary-bs020.html">21</a></li>
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<li><a href="._summary-bs021.html">22</a></li>
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<li><a href="._summary-bs022.html">23</a></li>
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<li><a href="._summary-bs023.html">24</a></li>
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<li><a href="._summary-bs024.html">25</a></li>
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<li><a href="._summary-bs025.html">26</a></li>
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<li><a href="._summary-bs026.html">27</a></li>
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<li><a href="._summary-bs027.html">28</a></li>
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<li><a href="">...</a></li>
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<li><a href="._summary-bs033.html">34</a></li>
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<li><a href="._summary-bs019.html">»</a></li>
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