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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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<!-- navigation toc: --> <li><a href="._week48-bs001.html#___sec0" style="font-size: 80%;"><b>Overview of week 48</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs002.html#___sec1" style="font-size: 80%;"><b>Thursday</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs003.html#___sec2" style="font-size: 80%;"><b>Friday</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs004.html#___sec3" style="font-size: 80%;"><b>Support Vector Machines, overarching aims</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs005.html#___sec4" style="font-size: 80%;"><b>Kernels and non-linearity</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs005.html#___sec5" style="font-size: 80%;">&nbsp;&nbsp;&nbsp;=</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs006.html#___sec6" style="font-size: 80%;"><b>The equations</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs007.html#___sec7" style="font-size: 80%;"><b>The problem to solve</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs008.html#___sec8" style="font-size: 80%;"><b>Tailoring the equations to the usage of CVXOPT</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs009.html#___sec9" style="font-size: 80%;"><b>Different kernels and Mercer's theorem</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs010.html#___sec10" style="font-size: 80%;"><b>The moons example ("Adapted from Geron, chapter 5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs011.html#___sec11" style="font-size: 80%;"><b>Mathematical optimization of convex functions</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs012.html#___sec12" style="font-size: 80%;"><b>How do we solve these problems?</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs013.html#___sec13" style="font-size: 80%;"><b>A simple example</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs014.html#___sec14" style="font-size: 80%;"><b>Back to the more realistic cases</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs015.html#___sec15" style="font-size: 80%;"><b>Setting up the matrices and the problem</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs016.html#___sec16" style="font-size: 80%;"><b>Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs017.html#___sec17" style="font-size: 80%;"><b>SVMs and Regression and multiclass classification</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs018.html#___sec18" style="font-size: 80%;"><b>Summary of course</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs019.html#___sec19" style="font-size: 80%;"><b>What? Me worry? No final exam in this course!</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs020.html#___sec20" style="font-size: 80%;"><b>Topics we have covered this year</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs021.html#___sec21" style="font-size: 80%;"><b>Statistical analysis and optimization of data</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs022.html#___sec22" style="font-size: 80%;"><b>Machine learning</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs023.html#___sec23" style="font-size: 80%;"><b>Learning outcomes and overarching aims of this course</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs024.html#___sec24" style="font-size: 80%;"><b>Perspective on Machine Learning</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs025.html#___sec25" style="font-size: 80%;"><b>Machine Learning Research</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs026.html#___sec26" style="font-size: 80%;"><b>Starting your Machine Learning Project</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs027.html#___sec27" style="font-size: 80%;"><b>Choose a Model and Algorithm</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs028.html#___sec28" style="font-size: 80%;"><b>Preparing Your Data</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs029.html#___sec29" style="font-size: 80%;"><b>Which Activation and Weights to Choose in Neural Networks</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs030.html#___sec30" style="font-size: 80%;"><b>Optimization Methods and Hyperparameters</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs031.html#___sec31" style="font-size: 80%;"><b>Resampling</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs032.html#___sec32" style="font-size: 80%;"><b>Other courses on Data science and Machine Learning at UiO</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs033.html#___sec33" style="font-size: 80%;"><b>Additional courses of interest</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs034.html#___sec34" style="font-size: 80%;"><b>What's the future like?</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs035.html#___sec35" style="font-size: 80%;"><b>Types of Machine Learning, a repetition</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs036.html#___sec36" style="font-size: 80%;"><b>Why Boltzmann machines?</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs037.html#___sec37" style="font-size: 80%;"><b>Boltzmann Machines</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs038.html#___sec38" style="font-size: 80%;"><b>Some similarities and differences from DNNs</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs039.html#___sec39" style="font-size: 80%;"><b>Boltzmann machines (BM)</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs040.html#___sec40" style="font-size: 80%;"><b>A standard BM setup</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs041.html#___sec41" style="font-size: 80%;"><b>The structure of the RBM network</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs042.html#___sec42" style="font-size: 80%;"><b>The network</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs043.html#___sec43" style="font-size: 80%;"><b>Goals</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs044.html#___sec44" style="font-size: 80%;"><b>Joint distribution</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs045.html#___sec45" style="font-size: 80%;"><b>Network Elements, the energy function</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs046.html#___sec46" style="font-size: 80%;"><b>Defining different types of RBMs</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs047.html#___sec47" style="font-size: 80%;"><b>More about RBMs</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs048.html#___sec48" style="font-size: 80%;"><b>Autoencoders: Overarching view</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs049.html#___sec49" style="font-size: 80%;"><b>Bayesian Machine Learning</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs050.html#___sec50" style="font-size: 80%;"><b>Reinforcement Learning</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs051.html#___sec51" style="font-size: 80%;"><b>Transfer learning</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs052.html#___sec52" style="font-size: 80%;"><b>Adversarial learning</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs053.html#___sec53" style="font-size: 80%;"><b>Dual learning</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs054.html#___sec54" style="font-size: 80%;"><b>Distributed machine learning</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs055.html#___sec55" style="font-size: 80%;"><b>Meta learning</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs056.html#___sec56" style="font-size: 80%;"><b>The Challenges Facing Machine Learning</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs057.html#___sec57" style="font-size: 80%;"><b>Explainable machine learning</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs058.html#___sec58" style="font-size: 80%;"><b>Quantum machine learning</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs059.html#___sec59" style="font-size: 80%;"><b>Quantum machine learning algorithms based on linear algebra</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs060.html#___sec60" style="font-size: 80%;"><b>Quantum reinforcement learning</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs061.html#___sec61" style="font-size: 80%;"><b>Quantum deep learning</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs062.html#___sec62" style="font-size: 80%;"><b>Social machine learning</b></a></li>
<!-- navigation toc: --> <li><a href="._week48-bs063.html#___sec63" style="font-size: 80%;"><b>The last words?</b></a></li>
<!-- navigation toc: --> <li><a href="#___sec64" style="font-size: 80%;"><b>Best wishes to you all and thanks so much for your heroic efforts this semester</b></a></li>
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<h2 id="___sec64" class="anchor">Best wishes to you all and thanks so much for your heroic efforts this semester </h2>
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