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Morten Hjorth-Jensen
2024-11-21 15:20:31 +01:00
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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>
<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%;">SVMs and Regression and multiclass classification</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs018.html#___sec17" style="font-size: 80%;">Summary of course</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs019.html#___sec18" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs020.html#___sec19" style="font-size: 80%;">Topics we have covered this year</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs021.html#___sec20" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs022.html#___sec21" style="font-size: 80%;">Machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs023.html#___sec22" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs024.html#___sec23" style="font-size: 80%;">Perspective on Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs025.html#___sec24" style="font-size: 80%;">Machine Learning Research</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs026.html#___sec25" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs027.html#___sec26" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs028.html#___sec27" style="font-size: 80%;">Preparing Your Data</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs029.html#___sec28" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs030.html#___sec29" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs031.html#___sec30" style="font-size: 80%;">Resampling</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs032.html#___sec31" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs033.html#___sec32" style="font-size: 80%;">Additional courses of interest</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs034.html#___sec33" style="font-size: 80%;">What's the future like?</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs035.html#___sec34" style="font-size: 80%;">Types of Machine Learning, a repetition</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs036.html#___sec35" style="font-size: 80%;">Why Boltzmann machines?</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs037.html#___sec36" style="font-size: 80%;">Boltzmann Machines</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs038.html#___sec37" style="font-size: 80%;">Some similarities and differences from DNNs</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs039.html#___sec38" style="font-size: 80%;">Boltzmann machines (BM)</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs040.html#___sec39" style="font-size: 80%;">A standard BM setup</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs041.html#___sec40" style="font-size: 80%;">The structure of the RBM network</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs042.html#___sec41" style="font-size: 80%;">The network</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs043.html#___sec42" style="font-size: 80%;">Goals</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs044.html#___sec43" style="font-size: 80%;">Joint distribution</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs045.html#___sec44" style="font-size: 80%;">Network Elements, the energy function</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs046.html#___sec45" style="font-size: 80%;">Defining different types of RBMs</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs047.html#___sec46" style="font-size: 80%;">More about RBMs</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs048.html#___sec47" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs049.html#___sec48" style="font-size: 80%;">Bayesian Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs050.html#___sec49" style="font-size: 80%;">Reinforcement Learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs051.html#___sec50" style="font-size: 80%;">Transfer learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs052.html#___sec51" style="font-size: 80%;">Adversarial learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs053.html#___sec52" style="font-size: 80%;">Dual learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs054.html#___sec53" style="font-size: 80%;">Distributed machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs055.html#___sec54" style="font-size: 80%;">Meta learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs056.html#___sec55" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs057.html#___sec56" style="font-size: 80%;">Explainable machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs058.html#___sec57" style="font-size: 80%;">Quantum machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs059.html#___sec58" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs060.html#___sec59" style="font-size: 80%;">Quantum reinforcement learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs061.html#___sec60" style="font-size: 80%;">Quantum deep learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs062.html#___sec61" style="font-size: 80%;">Social machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs063.html#___sec62" style="font-size: 80%;">The last words?</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs064.html#___sec63" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs001.html#overview-of-week-48" style="font-size: 80%;">Overview of week 48</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs002.html#plan-for-week-47" style="font-size: 80%;">Plan for week 47</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs003.html#boosting-a-bird-s-eye-view" style="font-size: 80%;">Boosting, a Bird's Eye View</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs004.html#what-is-boosting-additive-modelling-iterative-fitting" style="font-size: 80%;">What is boosting? Additive Modelling/Iterative Fitting</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs005.html#iterative-fitting-regression-and-squared-error-cost-function" style="font-size: 80%;">Iterative Fitting, Regression and Squared-error Cost Function</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs006.html#squared-error-example-and-iterative-fitting" style="font-size: 80%;">Squared-Error Example and Iterative Fitting</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs007.html#iterative-fitting-classification-and-adaboost" style="font-size: 80%;">Iterative Fitting, Classification and AdaBoost</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs008.html#adaptive-boosting-adaboost" style="font-size: 80%;">Adaptive Boosting, AdaBoost</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs009.html#building-up-adaboost" style="font-size: 80%;">Building up AdaBoost</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs010.html#adaptive-boosting-adaboost-basic-algorithm" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs011.html#basic-steps-of-adaboost" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs012.html#adaboost-examples" style="font-size: 80%;">AdaBoost Examples</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs013.html#making-an-adaboost-code-yourself" style="font-size: 80%;">Making an ADAboost code yourself</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs014.html#gradient-boosting-basics-with-steepest-descent-functional-gradient-descent" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs015.html#the-squared-error-again-steepest-descent" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs016.html#steepest-descent-example" style="font-size: 80%;">Steepest Descent Example</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs017.html#gradient-boosting-algorithm" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs018.html#gradient-boosting-examples-of-regression" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
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<center><h1>Week 48: Support Vector Machines and Summary of course</h1></center> <!-- document title -->
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<b>Morten Hjorth-Jensen</b> [1, 2]
<h1>Week 48: Support Vector Machines and Summary of course</h1>
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<b>Morten Hjorth-Jensen</b>
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<b>Department of Physics, University of Oslo, Norway</b>
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<center>[1] <b>Department of Physics, University of Oslo</b></center>
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
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<center><h4>Nov 29, 2020</h4></center> <!-- date -->
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<h4>Nov 21, 2024</h4>
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