274 lines
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HTML
274 lines
14 KiB
HTML
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<a class="navbar-brand" href="week48-bs.html">Week 48: Support Vector Machines and Summary of course</a>
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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="._week48-bs001.html#___sec0" style="font-size: 80%;">Overview of week 47</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs002.html#___sec1" style="font-size: 80%;">Thursday</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs003.html#___sec2" style="font-size: 80%;">Friday</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs004.html#___sec3" style="font-size: 80%;">Support Vector Machines, overarching aims</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs005.html#___sec4" style="font-size: 80%;">Kernels and non-linearity</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs006.html#___sec5" style="font-size: 80%;">The equations</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs007.html#___sec6" style="font-size: 80%;">The problem to solve</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs008.html#___sec7" style="font-size: 80%;">Different kernels and Mercer's theorem</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs009.html#___sec8" style="font-size: 80%;">The moons example</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs010.html#___sec9" style="font-size: 80%;">Mathematical optimization of convex functions</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs011.html#___sec10" style="font-size: 80%;">How do we solve these problems?</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs012.html#___sec11" style="font-size: 80%;">A simple example</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs013.html#___sec12" style="font-size: 80%;">Back to the more realistic cases</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs014.html#___sec13" style="font-size: 80%;">Summary of course</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs015.html#___sec14" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs016.html#___sec15" style="font-size: 80%;">Topics we have covered this year</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs017.html#___sec16" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs018.html#___sec17" style="font-size: 80%;">Machine learning</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs019.html#___sec18" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs020.html#___sec19" style="font-size: 80%;">Perspective on Machine Learning</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs021.html#___sec20" style="font-size: 80%;">Machine Learning Research</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs022.html#___sec21" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs023.html#___sec22" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs024.html#___sec23" style="font-size: 80%;">Preparing Your Data</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs025.html#___sec24" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs026.html#___sec25" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs027.html#___sec26" style="font-size: 80%;">Resampling</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs028.html#___sec27" 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="._week48-bs029.html#___sec28" style="font-size: 80%;">Additional courses of interest</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs030.html#___sec29" style="font-size: 80%;">What's the future like?</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs031.html#___sec30" style="font-size: 80%;">Bayesian Machine Learning</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs032.html#___sec31" style="font-size: 80%;">Reinforcement Learning</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs033.html#___sec32" style="font-size: 80%;">Transfer learning</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs034.html#___sec33" style="font-size: 80%;">Adversarial learning</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs035.html#___sec34" style="font-size: 80%;">Dual learning</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs036.html#___sec35" style="font-size: 80%;">Distributed machine learning</a></li>
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<!-- navigation toc: --> <li><a href="#___sec36" style="font-size: 80%;">Meta learning</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs038.html#___sec37" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs039.html#___sec38" style="font-size: 80%;">Explainable machine learning</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs040.html#___sec39" style="font-size: 80%;">Quantum machine learning</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs041.html#___sec40" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs042.html#___sec41" style="font-size: 80%;">Quantum reinforcement learning</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs043.html#___sec42" style="font-size: 80%;">Quantum deep learning</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs044.html#___sec43" style="font-size: 80%;">Social machine learning</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs045.html#___sec44" style="font-size: 80%;">The last words?</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs046.html#___sec45" 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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<!-- !split -->
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<h2 id="___sec36" class="anchor">Meta learning </h2>
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<p>
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Meta learning is an emerging research direction in machine
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learning. Roughly speaking, meta learning concerns learning how to
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learn, and focuses on the understanding and adaptation of the learning
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itself, instead of just completing a specific learning task. That is,
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a meta learner needs to be able to evaluate its own learning methods
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and adjust its own learning methods according to specific learning
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