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<!-- navigation toc: --> <li><a href="._week48-bs001.html#___sec0" style="font-size: 80%;">Overview of week 48</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>
|
||||
<!-- 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%;">Different kernels and Mercer's theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs009.html#___sec8" style="font-size: 80%;">The moons example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs010.html#___sec9" style="font-size: 80%;">Mathematical optimization of convex functions</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs011.html#___sec10" style="font-size: 80%;">How do we solve these problems?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs012.html#___sec11" style="font-size: 80%;">A simple example</a></li>
|
||||
<!-- 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>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs016.html#___sec15" style="font-size: 80%;">Topics we have covered this year</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs017.html#___sec16" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs018.html#___sec17" style="font-size: 80%;">Machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs019.html#___sec18" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs020.html#___sec19" style="font-size: 80%;">Perspective on Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs021.html#___sec20" style="font-size: 80%;">Machine Learning Research</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs022.html#___sec21" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs023.html#___sec22" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs024.html#___sec23" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs025.html#___sec24" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs026.html#___sec25" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs027.html#___sec26" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- 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>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs029.html#___sec28" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs030.html#___sec29" style="font-size: 80%;">What's the future like?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs031.html#___sec30" style="font-size: 80%;">Types of Machine Learning, a repetition</a></li>
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||||
<!-- navigation toc: --> <li><a href="._week48-bs032.html#___sec31" style="font-size: 80%;">Why Boltzmann machines?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs033.html#___sec32" style="font-size: 80%;">Boltzmann Machines</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs034.html#___sec33" style="font-size: 80%;">Some similarities and differences from DNNs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs035.html#___sec34" style="font-size: 80%;">Boltzmann machines (BM)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs036.html#___sec35" style="font-size: 80%;">A standard BM setup</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs037.html#___sec36" style="font-size: 80%;">The structure of the RBM network</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs038.html#___sec37" style="font-size: 80%;">The network</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs039.html#___sec38" style="font-size: 80%;">Goals</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs040.html#___sec39" style="font-size: 80%;">Joint distribution</a></li>
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||||
<!-- navigation toc: --> <li><a href="._week48-bs041.html#___sec40" style="font-size: 80%;">Network Elements, the energy function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs042.html#___sec41" style="font-size: 80%;">Defining different types of RBMs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs043.html#___sec42" style="font-size: 80%;">More about RBMs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs044.html#___sec43" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs045.html#___sec44" style="font-size: 80%;">Bayesian Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs046.html#___sec45" style="font-size: 80%;">Reinforcement Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec46" style="font-size: 80%;">Transfer learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs048.html#___sec47" style="font-size: 80%;">Adversarial learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs049.html#___sec48" style="font-size: 80%;">Dual learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs050.html#___sec49" style="font-size: 80%;">Distributed machine learning</a></li>
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||||
<!-- navigation toc: --> <li><a href="._week48-bs051.html#___sec50" style="font-size: 80%;">Meta learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs052.html#___sec51" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs053.html#___sec52" style="font-size: 80%;">Explainable machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs054.html#___sec53" style="font-size: 80%;">Quantum machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs055.html#___sec54" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs056.html#___sec55" style="font-size: 80%;">Quantum reinforcement learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs057.html#___sec56" style="font-size: 80%;">Quantum deep learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs058.html#___sec57" style="font-size: 80%;">Social machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs059.html#___sec58" style="font-size: 80%;">The last words?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs060.html#___sec59" 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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<h2 id="___sec46" class="anchor">Transfer learning </h2>
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The goal of transfer learning is to transfer the model or knowledge
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|
||||
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||||
|
||||
<p>
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||||
<a href="https://www.ias.edu/video/machinelearning/2020/0331-SamoryKpotufe" target="_self">Lecture on transfer learning</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 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%;">Different kernels and Mercer's theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs009.html#___sec8" style="font-size: 80%;">The moons example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs010.html#___sec9" style="font-size: 80%;">Mathematical optimization of convex functions</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs011.html#___sec10" style="font-size: 80%;">How do we solve these problems?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs012.html#___sec11" style="font-size: 80%;">A simple example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs013.html#___sec12" style="font-size: 80%;">Back to the more realistic cases</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs014.html#___sec13" style="font-size: 80%;">Summary of course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs015.html#___sec14" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs016.html#___sec15" style="font-size: 80%;">Topics we have covered this year</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs017.html#___sec16" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs018.html#___sec17" style="font-size: 80%;">Machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs019.html#___sec18" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs020.html#___sec19" style="font-size: 80%;">Perspective on Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs021.html#___sec20" style="font-size: 80%;">Machine Learning Research</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs022.html#___sec21" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs023.html#___sec22" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs024.html#___sec23" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs025.html#___sec24" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs026.html#___sec25" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs027.html#___sec26" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- 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>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs029.html#___sec28" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs030.html#___sec29" style="font-size: 80%;">What's the future like?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs031.html#___sec30" style="font-size: 80%;">Types of Machine Learning, a repetition</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs032.html#___sec31" style="font-size: 80%;">Why Boltzmann machines?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs033.html#___sec32" style="font-size: 80%;">Boltzmann Machines</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs034.html#___sec33" style="font-size: 80%;">Some similarities and differences from DNNs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs035.html#___sec34" style="font-size: 80%;">Boltzmann machines (BM)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs036.html#___sec35" style="font-size: 80%;">A standard BM setup</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs037.html#___sec36" style="font-size: 80%;">The structure of the RBM network</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs038.html#___sec37" style="font-size: 80%;">The network</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs039.html#___sec38" style="font-size: 80%;">Goals</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs040.html#___sec39" style="font-size: 80%;">Joint distribution</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs041.html#___sec40" style="font-size: 80%;">Network Elements, the energy function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs042.html#___sec41" style="font-size: 80%;">Defining different types of RBMs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs043.html#___sec42" style="font-size: 80%;">More about RBMs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs044.html#___sec43" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs045.html#___sec44" style="font-size: 80%;">Bayesian Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs046.html#___sec45" style="font-size: 80%;">Reinforcement Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs047.html#___sec46" style="font-size: 80%;">Transfer learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec47" style="font-size: 80%;">Adversarial learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs049.html#___sec48" style="font-size: 80%;">Dual learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs050.html#___sec49" style="font-size: 80%;">Distributed machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs051.html#___sec50" style="font-size: 80%;">Meta learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs052.html#___sec51" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs053.html#___sec52" style="font-size: 80%;">Explainable machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs054.html#___sec53" style="font-size: 80%;">Quantum machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs055.html#___sec54" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs056.html#___sec55" style="font-size: 80%;">Quantum reinforcement learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs057.html#___sec56" style="font-size: 80%;">Quantum deep learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs058.html#___sec57" style="font-size: 80%;">Social machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs059.html#___sec58" style="font-size: 80%;">The last words?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs060.html#___sec59" 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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<h2 id="___sec47" class="anchor">Adversarial learning </h2>
|
||||
|
||||
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|
||||
The conventional deep generative model has a potential problem: the
|
||||
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|
||||
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|
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|
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|
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|
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|
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|
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|
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||||
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|
||||
<a href="https://www.youtube.com/watch?v=CIfsB_EYsVI&ab_channel=StanfordUniversitySchoolofEngineering" target="_self">Lecture on adversial learning</a>.
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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 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%;">Different kernels and Mercer's theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs009.html#___sec8" style="font-size: 80%;">The moons example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs010.html#___sec9" style="font-size: 80%;">Mathematical optimization of convex functions</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs011.html#___sec10" style="font-size: 80%;">How do we solve these problems?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs012.html#___sec11" style="font-size: 80%;">A simple example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs013.html#___sec12" style="font-size: 80%;">Back to the more realistic cases</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs014.html#___sec13" style="font-size: 80%;">Summary of course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs015.html#___sec14" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs016.html#___sec15" style="font-size: 80%;">Topics we have covered this year</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs017.html#___sec16" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs018.html#___sec17" style="font-size: 80%;">Machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs019.html#___sec18" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs020.html#___sec19" style="font-size: 80%;">Perspective on Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs021.html#___sec20" style="font-size: 80%;">Machine Learning Research</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs022.html#___sec21" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs023.html#___sec22" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs024.html#___sec23" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs025.html#___sec24" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs026.html#___sec25" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs027.html#___sec26" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- 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>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs029.html#___sec28" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs030.html#___sec29" style="font-size: 80%;">What's the future like?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs031.html#___sec30" style="font-size: 80%;">Types of Machine Learning, a repetition</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs032.html#___sec31" style="font-size: 80%;">Why Boltzmann machines?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs033.html#___sec32" style="font-size: 80%;">Boltzmann Machines</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs034.html#___sec33" style="font-size: 80%;">Some similarities and differences from DNNs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs035.html#___sec34" style="font-size: 80%;">Boltzmann machines (BM)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs036.html#___sec35" style="font-size: 80%;">A standard BM setup</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs037.html#___sec36" style="font-size: 80%;">The structure of the RBM network</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs038.html#___sec37" style="font-size: 80%;">The network</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs039.html#___sec38" style="font-size: 80%;">Goals</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs040.html#___sec39" style="font-size: 80%;">Joint distribution</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs041.html#___sec40" style="font-size: 80%;">Network Elements, the energy function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs042.html#___sec41" style="font-size: 80%;">Defining different types of RBMs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs043.html#___sec42" style="font-size: 80%;">More about RBMs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs044.html#___sec43" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs045.html#___sec44" style="font-size: 80%;">Bayesian Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs046.html#___sec45" style="font-size: 80%;">Reinforcement Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs047.html#___sec46" style="font-size: 80%;">Transfer learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs048.html#___sec47" style="font-size: 80%;">Adversarial learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec48" style="font-size: 80%;">Dual learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs050.html#___sec49" style="font-size: 80%;">Distributed machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs051.html#___sec50" style="font-size: 80%;">Meta learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs052.html#___sec51" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs053.html#___sec52" style="font-size: 80%;">Explainable machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs054.html#___sec53" style="font-size: 80%;">Quantum machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs055.html#___sec54" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs056.html#___sec55" style="font-size: 80%;">Quantum reinforcement learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs057.html#___sec56" style="font-size: 80%;">Quantum deep learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs058.html#___sec57" style="font-size: 80%;">Social machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs059.html#___sec58" style="font-size: 80%;">The last words?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs060.html#___sec59" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
|
||||
|
||||
</ul>
|
||||
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|
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|
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|
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<!-- !split -->
|
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|
||||
<h2 id="___sec48" class="anchor">Dual learning </h2>
|
||||
|
||||
<p>
|
||||
Dual learning is a new learning paradigm, the basic idea of which is
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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||||
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|
||||
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|
||||
('Support Vector Machines, overarching aims', 2, None, '___sec3'),
|
||||
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|
||||
('The equations', 2, None, '___sec5'),
|
||||
('The problem to solve', 2, None, '___sec6'),
|
||||
("Different kernels and Mercer's theorem", 2, None, '___sec7'),
|
||||
('The moons example', 2, None, '___sec8'),
|
||||
('Mathematical optimization of convex functions',
|
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2,
|
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None,
|
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'___sec9'),
|
||||
('How do we solve these problems?', 2, None, '___sec10'),
|
||||
('A simple example', 2, None, '___sec11'),
|
||||
('Back to the more realistic cases', 2, None, '___sec12'),
|
||||
('Summary of course', 2, None, '___sec13'),
|
||||
('What? Me worry? No final exam in this course!',
|
||||
2,
|
||||
None,
|
||||
'___sec14'),
|
||||
('Topics we have covered this year', 2, None, '___sec15'),
|
||||
('Statistical analysis and optimization of data',
|
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2,
|
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|
||||
'___sec16'),
|
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2,
|
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'___sec18'),
|
||||
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||||
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|
||||
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|
||||
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|
||||
('Preparing Your Data', 2, None, '___sec23'),
|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
||||
2,
|
||||
None,
|
||||
'___sec24'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec25'),
|
||||
('Resampling', 2, None, '___sec26'),
|
||||
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|
||||
2,
|
||||
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|
||||
'___sec27'),
|
||||
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|
||||
("What's the future like?", 2, None, '___sec29'),
|
||||
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|
||||
('Why Boltzmann machines?', 2, None, '___sec31'),
|
||||
('Boltzmann Machines', 2, None, '___sec32'),
|
||||
('Some similarities and differences from DNNs',
|
||||
2,
|
||||
None,
|
||||
'___sec33'),
|
||||
('Boltzmann machines (BM)', 2, None, '___sec34'),
|
||||
('A standard BM setup', 2, None, '___sec35'),
|
||||
('The structure of the RBM network', 2, None, '___sec36'),
|
||||
('The network', 2, None, '___sec37'),
|
||||
('Goals', 2, None, '___sec38'),
|
||||
('Joint distribution', 2, None, '___sec39'),
|
||||
('Network Elements, the energy function', 2, None, '___sec40'),
|
||||
('Defining different types of RBMs', 2, None, '___sec41'),
|
||||
('More about RBMs', 2, None, '___sec42'),
|
||||
('Autoencoders: Overarching view', 2, None, '___sec43'),
|
||||
('Bayesian Machine Learning', 2, None, '___sec44'),
|
||||
('Reinforcement Learning', 2, None, '___sec45'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('The Challenges Facing Machine Learning', 2, None, '___sec51'),
|
||||
('Explainable machine learning', 2, None, '___sec52'),
|
||||
('Quantum machine learning', 2, None, '___sec53'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'___sec54'),
|
||||
('Quantum reinforcement learning', 2, None, '___sec55'),
|
||||
('Quantum deep learning', 2, None, '___sec56'),
|
||||
('Social machine learning', 2, None, '___sec57'),
|
||||
('The last words?', 2, None, '___sec58'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
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|
||||
2,
|
||||
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|
||||
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|
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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 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%;">Different kernels and Mercer's theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs009.html#___sec8" style="font-size: 80%;">The moons example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs010.html#___sec9" style="font-size: 80%;">Mathematical optimization of convex functions</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs011.html#___sec10" style="font-size: 80%;">How do we solve these problems?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs012.html#___sec11" style="font-size: 80%;">A simple example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs013.html#___sec12" style="font-size: 80%;">Back to the more realistic cases</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs014.html#___sec13" style="font-size: 80%;">Summary of course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs015.html#___sec14" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs016.html#___sec15" style="font-size: 80%;">Topics we have covered this year</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs017.html#___sec16" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs018.html#___sec17" style="font-size: 80%;">Machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs019.html#___sec18" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs020.html#___sec19" style="font-size: 80%;">Perspective on Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs021.html#___sec20" style="font-size: 80%;">Machine Learning Research</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs022.html#___sec21" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs023.html#___sec22" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs024.html#___sec23" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs025.html#___sec24" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs026.html#___sec25" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs027.html#___sec26" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- 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>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs029.html#___sec28" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs030.html#___sec29" style="font-size: 80%;">What's the future like?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs031.html#___sec30" style="font-size: 80%;">Types of Machine Learning, a repetition</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs032.html#___sec31" style="font-size: 80%;">Why Boltzmann machines?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs033.html#___sec32" style="font-size: 80%;">Boltzmann Machines</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs034.html#___sec33" style="font-size: 80%;">Some similarities and differences from DNNs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs035.html#___sec34" style="font-size: 80%;">Boltzmann machines (BM)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs036.html#___sec35" style="font-size: 80%;">A standard BM setup</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs037.html#___sec36" style="font-size: 80%;">The structure of the RBM network</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs038.html#___sec37" style="font-size: 80%;">The network</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs039.html#___sec38" style="font-size: 80%;">Goals</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs040.html#___sec39" style="font-size: 80%;">Joint distribution</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs041.html#___sec40" style="font-size: 80%;">Network Elements, the energy function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs042.html#___sec41" style="font-size: 80%;">Defining different types of RBMs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs043.html#___sec42" style="font-size: 80%;">More about RBMs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs044.html#___sec43" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs045.html#___sec44" style="font-size: 80%;">Bayesian Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs046.html#___sec45" style="font-size: 80%;">Reinforcement Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs047.html#___sec46" style="font-size: 80%;">Transfer learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs048.html#___sec47" style="font-size: 80%;">Adversarial learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs049.html#___sec48" style="font-size: 80%;">Dual learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec49" style="font-size: 80%;">Distributed machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs051.html#___sec50" style="font-size: 80%;">Meta learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs052.html#___sec51" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs053.html#___sec52" style="font-size: 80%;">Explainable machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs054.html#___sec53" style="font-size: 80%;">Quantum machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs055.html#___sec54" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs056.html#___sec55" style="font-size: 80%;">Quantum reinforcement learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs057.html#___sec56" style="font-size: 80%;">Quantum deep learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs058.html#___sec57" style="font-size: 80%;">Social machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs059.html#___sec58" style="font-size: 80%;">The last words?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs060.html#___sec59" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
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</ul>
|
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<a name="part0050"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec49" class="anchor">Distributed machine learning </h2>
|
||||
|
||||
<p>
|
||||
Distributed computation will speed up machine learning algorithms,
|
||||
significantly improve their efficiency, and thus enlarge their
|
||||
application. When distributed meets machine learning, more than just
|
||||
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{'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'),
|
||||
("Different kernels and Mercer's theorem", 2, None, '___sec7'),
|
||||
('The moons example', 2, None, '___sec8'),
|
||||
('Mathematical optimization of convex functions',
|
||||
2,
|
||||
None,
|
||||
'___sec9'),
|
||||
('How do we solve these problems?', 2, None, '___sec10'),
|
||||
('A simple example', 2, None, '___sec11'),
|
||||
('Back to the more realistic cases', 2, None, '___sec12'),
|
||||
('Summary of course', 2, None, '___sec13'),
|
||||
('What? Me worry? No final exam in this course!',
|
||||
2,
|
||||
None,
|
||||
'___sec14'),
|
||||
('Topics we have covered this year', 2, None, '___sec15'),
|
||||
('Statistical analysis and optimization of data',
|
||||
2,
|
||||
None,
|
||||
'___sec16'),
|
||||
('Machine learning', 2, None, '___sec17'),
|
||||
('Learning outcomes and overarching aims of this course',
|
||||
2,
|
||||
None,
|
||||
'___sec18'),
|
||||
('Perspective on Machine Learning', 2, None, '___sec19'),
|
||||
('Machine Learning Research', 2, None, '___sec20'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec21'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec22'),
|
||||
('Preparing Your Data', 2, None, '___sec23'),
|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
||||
2,
|
||||
None,
|
||||
'___sec24'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec25'),
|
||||
('Resampling', 2, None, '___sec26'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
||||
'___sec27'),
|
||||
('Additional courses of interest', 2, None, '___sec28'),
|
||||
("What's the future like?", 2, None, '___sec29'),
|
||||
('Types of Machine Learning, a repetition', 2, None, '___sec30'),
|
||||
('Why Boltzmann machines?', 2, None, '___sec31'),
|
||||
('Boltzmann Machines', 2, None, '___sec32'),
|
||||
('Some similarities and differences from DNNs',
|
||||
2,
|
||||
None,
|
||||
'___sec33'),
|
||||
('Boltzmann machines (BM)', 2, None, '___sec34'),
|
||||
('A standard BM setup', 2, None, '___sec35'),
|
||||
('The structure of the RBM network', 2, None, '___sec36'),
|
||||
('The network', 2, None, '___sec37'),
|
||||
('Goals', 2, None, '___sec38'),
|
||||
('Joint distribution', 2, None, '___sec39'),
|
||||
('Network Elements, the energy function', 2, None, '___sec40'),
|
||||
('Defining different types of RBMs', 2, None, '___sec41'),
|
||||
('More about RBMs', 2, None, '___sec42'),
|
||||
('Autoencoders: Overarching view', 2, None, '___sec43'),
|
||||
('Bayesian Machine Learning', 2, None, '___sec44'),
|
||||
('Reinforcement Learning', 2, None, '___sec45'),
|
||||
('Transfer learning', 2, None, '___sec46'),
|
||||
('Adversarial learning', 2, None, '___sec47'),
|
||||
('Dual learning', 2, None, '___sec48'),
|
||||
('Distributed machine learning', 2, None, '___sec49'),
|
||||
('Meta learning', 2, None, '___sec50'),
|
||||
('The Challenges Facing Machine Learning', 2, None, '___sec51'),
|
||||
('Explainable machine learning', 2, None, '___sec52'),
|
||||
('Quantum machine learning', 2, None, '___sec53'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'___sec54'),
|
||||
('Quantum reinforcement learning', 2, None, '___sec55'),
|
||||
('Quantum deep learning', 2, None, '___sec56'),
|
||||
('Social machine learning', 2, None, '___sec57'),
|
||||
('The last words?', 2, None, '___sec58'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'___sec59')]}
|
||||
end of tocinfo -->
|
||||
|
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|
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|
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|
||||
|
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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 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%;">Different kernels and Mercer's theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs009.html#___sec8" style="font-size: 80%;">The moons example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs010.html#___sec9" style="font-size: 80%;">Mathematical optimization of convex functions</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs011.html#___sec10" style="font-size: 80%;">How do we solve these problems?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs012.html#___sec11" style="font-size: 80%;">A simple example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs013.html#___sec12" style="font-size: 80%;">Back to the more realistic cases</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs014.html#___sec13" style="font-size: 80%;">Summary of course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs015.html#___sec14" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs016.html#___sec15" style="font-size: 80%;">Topics we have covered this year</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs017.html#___sec16" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs018.html#___sec17" style="font-size: 80%;">Machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs019.html#___sec18" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs020.html#___sec19" style="font-size: 80%;">Perspective on Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs021.html#___sec20" style="font-size: 80%;">Machine Learning Research</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs022.html#___sec21" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs023.html#___sec22" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs024.html#___sec23" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs025.html#___sec24" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs026.html#___sec25" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs027.html#___sec26" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- 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>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs029.html#___sec28" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs030.html#___sec29" style="font-size: 80%;">What's the future like?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs031.html#___sec30" style="font-size: 80%;">Types of Machine Learning, a repetition</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs032.html#___sec31" style="font-size: 80%;">Why Boltzmann machines?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs033.html#___sec32" style="font-size: 80%;">Boltzmann Machines</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs034.html#___sec33" style="font-size: 80%;">Some similarities and differences from DNNs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs035.html#___sec34" style="font-size: 80%;">Boltzmann machines (BM)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs036.html#___sec35" style="font-size: 80%;">A standard BM setup</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs037.html#___sec36" style="font-size: 80%;">The structure of the RBM network</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs038.html#___sec37" style="font-size: 80%;">The network</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs039.html#___sec38" style="font-size: 80%;">Goals</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs040.html#___sec39" style="font-size: 80%;">Joint distribution</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs041.html#___sec40" style="font-size: 80%;">Network Elements, the energy function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs042.html#___sec41" style="font-size: 80%;">Defining different types of RBMs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs043.html#___sec42" style="font-size: 80%;">More about RBMs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs044.html#___sec43" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs045.html#___sec44" style="font-size: 80%;">Bayesian Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs046.html#___sec45" style="font-size: 80%;">Reinforcement Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs047.html#___sec46" style="font-size: 80%;">Transfer learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs048.html#___sec47" style="font-size: 80%;">Adversarial learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs049.html#___sec48" style="font-size: 80%;">Dual learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs050.html#___sec49" style="font-size: 80%;">Distributed machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec50" style="font-size: 80%;">Meta learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs052.html#___sec51" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs053.html#___sec52" style="font-size: 80%;">Explainable machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs054.html#___sec53" style="font-size: 80%;">Quantum machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs055.html#___sec54" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs056.html#___sec55" style="font-size: 80%;">Quantum reinforcement learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs057.html#___sec56" style="font-size: 80%;">Quantum deep learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs058.html#___sec57" style="font-size: 80%;">Social machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs059.html#___sec58" style="font-size: 80%;">The last words?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs060.html#___sec59" 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>
|
||||
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|
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|
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|
||||
<p> </p><p> </p><p> </p> <!-- add vertical space -->
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||||
|
||||
<a name="part0051"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec50" class="anchor">Meta learning </h2>
|
||||
|
||||
<p>
|
||||
Meta learning is an emerging research direction in machine
|
||||
learning. Roughly speaking, meta learning concerns learning how to
|
||||
learn, and focuses on the understanding and adaptation of the learning
|
||||
itself, instead of just completing a specific learning task. That is,
|
||||
a meta learner needs to be able to evaluate its own learning methods
|
||||
and adjust its own learning methods according to specific learning
|
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tasks.
|
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|
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|
||||
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<li><a href="._week48-bs050.html">«</a></li>
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|
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{'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'),
|
||||
("Different kernels and Mercer's theorem", 2, None, '___sec7'),
|
||||
('The moons example', 2, None, '___sec8'),
|
||||
('Mathematical optimization of convex functions',
|
||||
2,
|
||||
None,
|
||||
'___sec9'),
|
||||
('How do we solve these problems?', 2, None, '___sec10'),
|
||||
('A simple example', 2, None, '___sec11'),
|
||||
('Back to the more realistic cases', 2, None, '___sec12'),
|
||||
('Summary of course', 2, None, '___sec13'),
|
||||
('What? Me worry? No final exam in this course!',
|
||||
2,
|
||||
None,
|
||||
'___sec14'),
|
||||
('Topics we have covered this year', 2, None, '___sec15'),
|
||||
('Statistical analysis and optimization of data',
|
||||
2,
|
||||
None,
|
||||
'___sec16'),
|
||||
('Machine learning', 2, None, '___sec17'),
|
||||
('Learning outcomes and overarching aims of this course',
|
||||
2,
|
||||
None,
|
||||
'___sec18'),
|
||||
('Perspective on Machine Learning', 2, None, '___sec19'),
|
||||
('Machine Learning Research', 2, None, '___sec20'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec21'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec22'),
|
||||
('Preparing Your Data', 2, None, '___sec23'),
|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
||||
2,
|
||||
None,
|
||||
'___sec24'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec25'),
|
||||
('Resampling', 2, None, '___sec26'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
||||
'___sec27'),
|
||||
('Additional courses of interest', 2, None, '___sec28'),
|
||||
("What's the future like?", 2, None, '___sec29'),
|
||||
('Types of Machine Learning, a repetition', 2, None, '___sec30'),
|
||||
('Why Boltzmann machines?', 2, None, '___sec31'),
|
||||
('Boltzmann Machines', 2, None, '___sec32'),
|
||||
('Some similarities and differences from DNNs',
|
||||
2,
|
||||
None,
|
||||
'___sec33'),
|
||||
('Boltzmann machines (BM)', 2, None, '___sec34'),
|
||||
('A standard BM setup', 2, None, '___sec35'),
|
||||
('The structure of the RBM network', 2, None, '___sec36'),
|
||||
('The network', 2, None, '___sec37'),
|
||||
('Goals', 2, None, '___sec38'),
|
||||
('Joint distribution', 2, None, '___sec39'),
|
||||
('Network Elements, the energy function', 2, None, '___sec40'),
|
||||
('Defining different types of RBMs', 2, None, '___sec41'),
|
||||
('More about RBMs', 2, None, '___sec42'),
|
||||
('Autoencoders: Overarching view', 2, None, '___sec43'),
|
||||
('Bayesian Machine Learning', 2, None, '___sec44'),
|
||||
('Reinforcement Learning', 2, None, '___sec45'),
|
||||
('Transfer learning', 2, None, '___sec46'),
|
||||
('Adversarial learning', 2, None, '___sec47'),
|
||||
('Dual learning', 2, None, '___sec48'),
|
||||
('Distributed machine learning', 2, None, '___sec49'),
|
||||
('Meta learning', 2, None, '___sec50'),
|
||||
('The Challenges Facing Machine Learning', 2, None, '___sec51'),
|
||||
('Explainable machine learning', 2, None, '___sec52'),
|
||||
('Quantum machine learning', 2, None, '___sec53'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'___sec54'),
|
||||
('Quantum reinforcement learning', 2, None, '___sec55'),
|
||||
('Quantum deep learning', 2, None, '___sec56'),
|
||||
('Social machine learning', 2, None, '___sec57'),
|
||||
('The last words?', 2, None, '___sec58'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'___sec59')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
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|
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|
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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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</div>
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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 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%;">Different kernels and Mercer's theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs009.html#___sec8" style="font-size: 80%;">The moons example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs010.html#___sec9" style="font-size: 80%;">Mathematical optimization of convex functions</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs011.html#___sec10" style="font-size: 80%;">How do we solve these problems?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs012.html#___sec11" style="font-size: 80%;">A simple example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs013.html#___sec12" style="font-size: 80%;">Back to the more realistic cases</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs014.html#___sec13" style="font-size: 80%;">Summary of course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs015.html#___sec14" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs016.html#___sec15" style="font-size: 80%;">Topics we have covered this year</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs017.html#___sec16" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs018.html#___sec17" style="font-size: 80%;">Machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs019.html#___sec18" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs020.html#___sec19" style="font-size: 80%;">Perspective on Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs021.html#___sec20" style="font-size: 80%;">Machine Learning Research</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs022.html#___sec21" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs023.html#___sec22" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs024.html#___sec23" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs025.html#___sec24" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs026.html#___sec25" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs027.html#___sec26" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- 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>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs029.html#___sec28" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs030.html#___sec29" style="font-size: 80%;">What's the future like?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs031.html#___sec30" style="font-size: 80%;">Types of Machine Learning, a repetition</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs032.html#___sec31" style="font-size: 80%;">Why Boltzmann machines?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs033.html#___sec32" style="font-size: 80%;">Boltzmann Machines</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs034.html#___sec33" style="font-size: 80%;">Some similarities and differences from DNNs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs035.html#___sec34" style="font-size: 80%;">Boltzmann machines (BM)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs036.html#___sec35" style="font-size: 80%;">A standard BM setup</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs037.html#___sec36" style="font-size: 80%;">The structure of the RBM network</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs038.html#___sec37" style="font-size: 80%;">The network</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs039.html#___sec38" style="font-size: 80%;">Goals</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs040.html#___sec39" style="font-size: 80%;">Joint distribution</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs041.html#___sec40" style="font-size: 80%;">Network Elements, the energy function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs042.html#___sec41" style="font-size: 80%;">Defining different types of RBMs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs043.html#___sec42" style="font-size: 80%;">More about RBMs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs044.html#___sec43" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs045.html#___sec44" style="font-size: 80%;">Bayesian Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs046.html#___sec45" style="font-size: 80%;">Reinforcement Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs047.html#___sec46" style="font-size: 80%;">Transfer learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs048.html#___sec47" style="font-size: 80%;">Adversarial learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs049.html#___sec48" style="font-size: 80%;">Dual learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs050.html#___sec49" style="font-size: 80%;">Distributed machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs051.html#___sec50" style="font-size: 80%;">Meta learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec51" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs053.html#___sec52" style="font-size: 80%;">Explainable machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs054.html#___sec53" style="font-size: 80%;">Quantum machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs055.html#___sec54" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs056.html#___sec55" style="font-size: 80%;">Quantum reinforcement learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs057.html#___sec56" style="font-size: 80%;">Quantum deep learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs058.html#___sec57" style="font-size: 80%;">Social machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs059.html#___sec58" style="font-size: 80%;">The last words?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs060.html#___sec59" 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>
|
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|
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<div class="container">
|
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|
||||
<p> </p><p> </p><p> </p> <!-- add vertical space -->
|
||||
|
||||
<a name="part0052"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec51" class="anchor">The Challenges Facing Machine Learning </h2>
|
||||
|
||||
<p>
|
||||
While there has been much progress in machine learning, there are also challenges.
|
||||
|
||||
<p>
|
||||
For example, the mainstream machine learning technologies are
|
||||
black-box approaches, making us concerned about their potential
|
||||
risks. To tackle this challenge, we may want to make machine learning
|
||||
more explainable and controllable. As another example, the
|
||||
computational complexity of machine learning algorithms is usually
|
||||
very high and we may want to invent lightweight algorithms or
|
||||
implementations. Furthermore, in many domains such as physics,
|
||||
chemistry, biology, and social sciences, people usually seek elegantly
|
||||
simple equations (e.g., the Schrödinger equation) to uncover the
|
||||
underlying laws behind various phenomena. In the field of machine
|
||||
learning, can we reveal simple laws instead of designing more complex
|
||||
models for data fitting? Although there are many challenges, we are
|
||||
still very optimistic about the future of machine learning. As we look
|
||||
forward to the future, here are what we think the research hotspots in
|
||||
the next ten years will be.
|
||||
|
||||
<p>
|
||||
<p>
|
||||
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<li><a href="._week48-bs060.html">61</a></li>
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{'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'),
|
||||
("Different kernels and Mercer's theorem", 2, None, '___sec7'),
|
||||
('The moons example', 2, None, '___sec8'),
|
||||
('Mathematical optimization of convex functions',
|
||||
2,
|
||||
None,
|
||||
'___sec9'),
|
||||
('How do we solve these problems?', 2, None, '___sec10'),
|
||||
('A simple example', 2, None, '___sec11'),
|
||||
('Back to the more realistic cases', 2, None, '___sec12'),
|
||||
('Summary of course', 2, None, '___sec13'),
|
||||
('What? Me worry? No final exam in this course!',
|
||||
2,
|
||||
None,
|
||||
'___sec14'),
|
||||
('Topics we have covered this year', 2, None, '___sec15'),
|
||||
('Statistical analysis and optimization of data',
|
||||
2,
|
||||
None,
|
||||
'___sec16'),
|
||||
('Machine learning', 2, None, '___sec17'),
|
||||
('Learning outcomes and overarching aims of this course',
|
||||
2,
|
||||
None,
|
||||
'___sec18'),
|
||||
('Perspective on Machine Learning', 2, None, '___sec19'),
|
||||
('Machine Learning Research', 2, None, '___sec20'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec21'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec22'),
|
||||
('Preparing Your Data', 2, None, '___sec23'),
|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
||||
2,
|
||||
None,
|
||||
'___sec24'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec25'),
|
||||
('Resampling', 2, None, '___sec26'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
||||
'___sec27'),
|
||||
('Additional courses of interest', 2, None, '___sec28'),
|
||||
("What's the future like?", 2, None, '___sec29'),
|
||||
('Types of Machine Learning, a repetition', 2, None, '___sec30'),
|
||||
('Why Boltzmann machines?', 2, None, '___sec31'),
|
||||
('Boltzmann Machines', 2, None, '___sec32'),
|
||||
('Some similarities and differences from DNNs',
|
||||
2,
|
||||
None,
|
||||
'___sec33'),
|
||||
('Boltzmann machines (BM)', 2, None, '___sec34'),
|
||||
('A standard BM setup', 2, None, '___sec35'),
|
||||
('The structure of the RBM network', 2, None, '___sec36'),
|
||||
('The network', 2, None, '___sec37'),
|
||||
('Goals', 2, None, '___sec38'),
|
||||
('Joint distribution', 2, None, '___sec39'),
|
||||
('Network Elements, the energy function', 2, None, '___sec40'),
|
||||
('Defining different types of RBMs', 2, None, '___sec41'),
|
||||
('More about RBMs', 2, None, '___sec42'),
|
||||
('Autoencoders: Overarching view', 2, None, '___sec43'),
|
||||
('Bayesian Machine Learning', 2, None, '___sec44'),
|
||||
('Reinforcement Learning', 2, None, '___sec45'),
|
||||
('Transfer learning', 2, None, '___sec46'),
|
||||
('Adversarial learning', 2, None, '___sec47'),
|
||||
('Dual learning', 2, None, '___sec48'),
|
||||
('Distributed machine learning', 2, None, '___sec49'),
|
||||
('Meta learning', 2, None, '___sec50'),
|
||||
('The Challenges Facing Machine Learning', 2, None, '___sec51'),
|
||||
('Explainable machine learning', 2, None, '___sec52'),
|
||||
('Quantum machine learning', 2, None, '___sec53'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'___sec54'),
|
||||
('Quantum reinforcement learning', 2, None, '___sec55'),
|
||||
('Quantum deep learning', 2, None, '___sec56'),
|
||||
('Social machine learning', 2, None, '___sec57'),
|
||||
('The last words?', 2, None, '___sec58'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'___sec59')]}
|
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|
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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 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%;">Different kernels and Mercer's theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs009.html#___sec8" style="font-size: 80%;">The moons example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs010.html#___sec9" style="font-size: 80%;">Mathematical optimization of convex functions</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs011.html#___sec10" style="font-size: 80%;">How do we solve these problems?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs012.html#___sec11" style="font-size: 80%;">A simple example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs013.html#___sec12" style="font-size: 80%;">Back to the more realistic cases</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs014.html#___sec13" style="font-size: 80%;">Summary of course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs015.html#___sec14" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs016.html#___sec15" style="font-size: 80%;">Topics we have covered this year</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs017.html#___sec16" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs018.html#___sec17" style="font-size: 80%;">Machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs019.html#___sec18" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs020.html#___sec19" style="font-size: 80%;">Perspective on Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs021.html#___sec20" style="font-size: 80%;">Machine Learning Research</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs022.html#___sec21" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs023.html#___sec22" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs024.html#___sec23" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs025.html#___sec24" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs026.html#___sec25" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs027.html#___sec26" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- 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>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs029.html#___sec28" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs030.html#___sec29" style="font-size: 80%;">What's the future like?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs031.html#___sec30" style="font-size: 80%;">Types of Machine Learning, a repetition</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs032.html#___sec31" style="font-size: 80%;">Why Boltzmann machines?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs033.html#___sec32" style="font-size: 80%;">Boltzmann Machines</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs034.html#___sec33" style="font-size: 80%;">Some similarities and differences from DNNs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs035.html#___sec34" style="font-size: 80%;">Boltzmann machines (BM)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs036.html#___sec35" style="font-size: 80%;">A standard BM setup</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs037.html#___sec36" style="font-size: 80%;">The structure of the RBM network</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs038.html#___sec37" style="font-size: 80%;">The network</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs039.html#___sec38" style="font-size: 80%;">Goals</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs040.html#___sec39" style="font-size: 80%;">Joint distribution</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs041.html#___sec40" style="font-size: 80%;">Network Elements, the energy function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs042.html#___sec41" style="font-size: 80%;">Defining different types of RBMs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs043.html#___sec42" style="font-size: 80%;">More about RBMs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs044.html#___sec43" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs045.html#___sec44" style="font-size: 80%;">Bayesian Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs046.html#___sec45" style="font-size: 80%;">Reinforcement Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs047.html#___sec46" style="font-size: 80%;">Transfer learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs048.html#___sec47" style="font-size: 80%;">Adversarial learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs049.html#___sec48" style="font-size: 80%;">Dual learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs050.html#___sec49" style="font-size: 80%;">Distributed machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs051.html#___sec50" style="font-size: 80%;">Meta learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs052.html#___sec51" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec52" style="font-size: 80%;">Explainable machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs054.html#___sec53" style="font-size: 80%;">Quantum machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs055.html#___sec54" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs056.html#___sec55" style="font-size: 80%;">Quantum reinforcement learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs057.html#___sec56" style="font-size: 80%;">Quantum deep learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs058.html#___sec57" style="font-size: 80%;">Social machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs059.html#___sec58" style="font-size: 80%;">The last words?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs060.html#___sec59" 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>
|
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|
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|
||||
<p> </p><p> </p><p> </p> <!-- add vertical space -->
|
||||
|
||||
<a name="part0053"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec52" class="anchor">Explainable machine learning </h2>
|
||||
|
||||
<p>
|
||||
Machine learning, especially deep learning, evolves rapidly. The
|
||||
ability gap between machine and human on many complex cognitive tasks
|
||||
becomes narrower and narrower. However, we are still in the very early
|
||||
stage in terms of explaining why those effective models work and how
|
||||
they work.
|
||||
|
||||
<p>
|
||||
What is missing: the gap between correlation and causation Most
|
||||
machine learning techniques, especially the statistical ones, depend
|
||||
highly on data correlation to make predictions and analyses. In
|
||||
contrast, rational humans tend to reply on clear and trustworthy
|
||||
causality relations obtained via logical reasoning on real and clear
|
||||
facts. It is one of the core goals of explainable machine learning to
|
||||
transition from solving problems by data correlation to solving
|
||||
problems by logical reasoning.
|
||||
|
||||
<p>
|
||||
<p>
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<li><a href="._week48-bs052.html">«</a></li>
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||||
{'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'),
|
||||
("Different kernels and Mercer's theorem", 2, None, '___sec7'),
|
||||
('The moons example', 2, None, '___sec8'),
|
||||
('Mathematical optimization of convex functions',
|
||||
2,
|
||||
None,
|
||||
'___sec9'),
|
||||
('How do we solve these problems?', 2, None, '___sec10'),
|
||||
('A simple example', 2, None, '___sec11'),
|
||||
('Back to the more realistic cases', 2, None, '___sec12'),
|
||||
('Summary of course', 2, None, '___sec13'),
|
||||
('What? Me worry? No final exam in this course!',
|
||||
2,
|
||||
None,
|
||||
'___sec14'),
|
||||
('Topics we have covered this year', 2, None, '___sec15'),
|
||||
('Statistical analysis and optimization of data',
|
||||
2,
|
||||
None,
|
||||
'___sec16'),
|
||||
('Machine learning', 2, None, '___sec17'),
|
||||
('Learning outcomes and overarching aims of this course',
|
||||
2,
|
||||
None,
|
||||
'___sec18'),
|
||||
('Perspective on Machine Learning', 2, None, '___sec19'),
|
||||
('Machine Learning Research', 2, None, '___sec20'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec21'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec22'),
|
||||
('Preparing Your Data', 2, None, '___sec23'),
|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
||||
2,
|
||||
None,
|
||||
'___sec24'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec25'),
|
||||
('Resampling', 2, None, '___sec26'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
||||
'___sec27'),
|
||||
('Additional courses of interest', 2, None, '___sec28'),
|
||||
("What's the future like?", 2, None, '___sec29'),
|
||||
('Types of Machine Learning, a repetition', 2, None, '___sec30'),
|
||||
('Why Boltzmann machines?', 2, None, '___sec31'),
|
||||
('Boltzmann Machines', 2, None, '___sec32'),
|
||||
('Some similarities and differences from DNNs',
|
||||
2,
|
||||
None,
|
||||
'___sec33'),
|
||||
('Boltzmann machines (BM)', 2, None, '___sec34'),
|
||||
('A standard BM setup', 2, None, '___sec35'),
|
||||
('The structure of the RBM network', 2, None, '___sec36'),
|
||||
('The network', 2, None, '___sec37'),
|
||||
('Goals', 2, None, '___sec38'),
|
||||
('Joint distribution', 2, None, '___sec39'),
|
||||
('Network Elements, the energy function', 2, None, '___sec40'),
|
||||
('Defining different types of RBMs', 2, None, '___sec41'),
|
||||
('More about RBMs', 2, None, '___sec42'),
|
||||
('Autoencoders: Overarching view', 2, None, '___sec43'),
|
||||
('Bayesian Machine Learning', 2, None, '___sec44'),
|
||||
('Reinforcement Learning', 2, None, '___sec45'),
|
||||
('Transfer learning', 2, None, '___sec46'),
|
||||
('Adversarial learning', 2, None, '___sec47'),
|
||||
('Dual learning', 2, None, '___sec48'),
|
||||
('Distributed machine learning', 2, None, '___sec49'),
|
||||
('Meta learning', 2, None, '___sec50'),
|
||||
('The Challenges Facing Machine Learning', 2, None, '___sec51'),
|
||||
('Explainable machine learning', 2, None, '___sec52'),
|
||||
('Quantum machine learning', 2, None, '___sec53'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'___sec54'),
|
||||
('Quantum reinforcement learning', 2, None, '___sec55'),
|
||||
('Quantum deep learning', 2, None, '___sec56'),
|
||||
('Social machine learning', 2, None, '___sec57'),
|
||||
('The last words?', 2, None, '___sec58'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'___sec59')]}
|
||||
end of tocinfo -->
|
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|
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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 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%;">Different kernels and Mercer's theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs009.html#___sec8" style="font-size: 80%;">The moons example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs010.html#___sec9" style="font-size: 80%;">Mathematical optimization of convex functions</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs011.html#___sec10" style="font-size: 80%;">How do we solve these problems?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs012.html#___sec11" style="font-size: 80%;">A simple example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs013.html#___sec12" style="font-size: 80%;">Back to the more realistic cases</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs014.html#___sec13" style="font-size: 80%;">Summary of course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs015.html#___sec14" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs016.html#___sec15" style="font-size: 80%;">Topics we have covered this year</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs017.html#___sec16" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs018.html#___sec17" style="font-size: 80%;">Machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs019.html#___sec18" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs020.html#___sec19" style="font-size: 80%;">Perspective on Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs021.html#___sec20" style="font-size: 80%;">Machine Learning Research</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs022.html#___sec21" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs023.html#___sec22" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs024.html#___sec23" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs025.html#___sec24" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs026.html#___sec25" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs027.html#___sec26" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- 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>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs029.html#___sec28" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs030.html#___sec29" style="font-size: 80%;">What's the future like?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs031.html#___sec30" style="font-size: 80%;">Types of Machine Learning, a repetition</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs032.html#___sec31" style="font-size: 80%;">Why Boltzmann machines?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs033.html#___sec32" style="font-size: 80%;">Boltzmann Machines</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs034.html#___sec33" style="font-size: 80%;">Some similarities and differences from DNNs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs035.html#___sec34" style="font-size: 80%;">Boltzmann machines (BM)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs036.html#___sec35" style="font-size: 80%;">A standard BM setup</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs037.html#___sec36" style="font-size: 80%;">The structure of the RBM network</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs038.html#___sec37" style="font-size: 80%;">The network</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs039.html#___sec38" style="font-size: 80%;">Goals</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs040.html#___sec39" style="font-size: 80%;">Joint distribution</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs041.html#___sec40" style="font-size: 80%;">Network Elements, the energy function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs042.html#___sec41" style="font-size: 80%;">Defining different types of RBMs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs043.html#___sec42" style="font-size: 80%;">More about RBMs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs044.html#___sec43" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs045.html#___sec44" style="font-size: 80%;">Bayesian Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs046.html#___sec45" style="font-size: 80%;">Reinforcement Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs047.html#___sec46" style="font-size: 80%;">Transfer learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs048.html#___sec47" style="font-size: 80%;">Adversarial learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs049.html#___sec48" style="font-size: 80%;">Dual learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs050.html#___sec49" style="font-size: 80%;">Distributed machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs051.html#___sec50" style="font-size: 80%;">Meta learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs052.html#___sec51" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs053.html#___sec52" style="font-size: 80%;">Explainable machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec53" style="font-size: 80%;">Quantum machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs055.html#___sec54" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs056.html#___sec55" style="font-size: 80%;">Quantum reinforcement learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs057.html#___sec56" style="font-size: 80%;">Quantum deep learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs058.html#___sec57" style="font-size: 80%;">Social machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs059.html#___sec58" style="font-size: 80%;">The last words?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs060.html#___sec59" 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>
|
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|
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|
||||
<div class="container">
|
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|
||||
<p> </p><p> </p><p> </p> <!-- add vertical space -->
|
||||
|
||||
<a name="part0054"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec53" class="anchor">Quantum machine learning </h2>
|
||||
|
||||
<p>
|
||||
Quantum machine learning is an emerging interdisciplinary research
|
||||
area at the intersection of quantum computing and machine learning.
|
||||
|
||||
<p>
|
||||
Quantum computers use effects such as quantum coherence and quantum
|
||||
entanglement to process information, which is fundamentally different
|
||||
from classical computers. Quantum algorithms have surpassed the best
|
||||
classical algorithms in several problems (e.g., searching for an
|
||||
unsorted database, inverting a sparse matrix), which we call quantum
|
||||
acceleration.
|
||||
|
||||
<p>
|
||||
When quantum computing meets machine learning, it can be a mutually
|
||||
beneficial and reinforcing process, as it allows us to take advantage
|
||||
of quantum computing to improve the performance of classical machine
|
||||
learning algorithms. In addition, we can also use the machine learning
|
||||
algorithms (on classic computers) to analyze and improve quantum
|
||||
computing systems.
|
||||
|
||||
<p>
|
||||
<a href="https://www.youtube.com/watch?v=Lbndu5EIWvI&ab_channel=%E6%85%B6%E6%87%89%E7%BE%A9%E5%A1%BEKeioUniversity" target="_self">Lecture on Quantum ML</a>.
|
||||
|
||||
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|
||||
<p>
|
||||
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<li><a href="._week48-bs046.html">47</a></li>
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|
||||
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|
||||
('Thursday', 2, None, '___sec1'),
|
||||
('Friday', 2, None, '___sec2'),
|
||||
('Support Vector Machines, overarching aims', 2, None, '___sec3'),
|
||||
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|
||||
('The equations', 2, None, '___sec5'),
|
||||
('The problem to solve', 2, None, '___sec6'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('Summary of course', 2, None, '___sec13'),
|
||||
('What? Me worry? No final exam in this course!',
|
||||
2,
|
||||
None,
|
||||
'___sec14'),
|
||||
('Topics we have covered this year', 2, None, '___sec15'),
|
||||
('Statistical analysis and optimization of data',
|
||||
2,
|
||||
None,
|
||||
'___sec16'),
|
||||
('Machine learning', 2, None, '___sec17'),
|
||||
('Learning outcomes and overarching aims of this course',
|
||||
2,
|
||||
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|
||||
'___sec18'),
|
||||
('Perspective on Machine Learning', 2, None, '___sec19'),
|
||||
('Machine Learning Research', 2, None, '___sec20'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec21'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec22'),
|
||||
('Preparing Your Data', 2, None, '___sec23'),
|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
||||
2,
|
||||
None,
|
||||
'___sec24'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec25'),
|
||||
('Resampling', 2, None, '___sec26'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
||||
'___sec27'),
|
||||
('Additional courses of interest', 2, None, '___sec28'),
|
||||
("What's the future like?", 2, None, '___sec29'),
|
||||
('Types of Machine Learning, a repetition', 2, None, '___sec30'),
|
||||
('Why Boltzmann machines?', 2, None, '___sec31'),
|
||||
('Boltzmann Machines', 2, None, '___sec32'),
|
||||
('Some similarities and differences from DNNs',
|
||||
2,
|
||||
None,
|
||||
'___sec33'),
|
||||
('Boltzmann machines (BM)', 2, None, '___sec34'),
|
||||
('A standard BM setup', 2, None, '___sec35'),
|
||||
('The structure of the RBM network', 2, None, '___sec36'),
|
||||
('The network', 2, None, '___sec37'),
|
||||
('Goals', 2, None, '___sec38'),
|
||||
('Joint distribution', 2, None, '___sec39'),
|
||||
('Network Elements, the energy function', 2, None, '___sec40'),
|
||||
('Defining different types of RBMs', 2, None, '___sec41'),
|
||||
('More about RBMs', 2, None, '___sec42'),
|
||||
('Autoencoders: Overarching view', 2, None, '___sec43'),
|
||||
('Bayesian Machine Learning', 2, None, '___sec44'),
|
||||
('Reinforcement Learning', 2, None, '___sec45'),
|
||||
('Transfer learning', 2, None, '___sec46'),
|
||||
('Adversarial learning', 2, None, '___sec47'),
|
||||
('Dual learning', 2, None, '___sec48'),
|
||||
('Distributed machine learning', 2, None, '___sec49'),
|
||||
('Meta learning', 2, None, '___sec50'),
|
||||
('The Challenges Facing Machine Learning', 2, None, '___sec51'),
|
||||
('Explainable machine learning', 2, None, '___sec52'),
|
||||
('Quantum machine learning', 2, None, '___sec53'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'___sec54'),
|
||||
('Quantum reinforcement learning', 2, None, '___sec55'),
|
||||
('Quantum deep learning', 2, None, '___sec56'),
|
||||
('Social machine learning', 2, None, '___sec57'),
|
||||
('The last words?', 2, None, '___sec58'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'___sec59')]}
|
||||
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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%;">Overview of week 48</a></li>
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<!-- 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%;">Different kernels and Mercer's theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs009.html#___sec8" style="font-size: 80%;">The moons example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs010.html#___sec9" style="font-size: 80%;">Mathematical optimization of convex functions</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs011.html#___sec10" style="font-size: 80%;">How do we solve these problems?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs012.html#___sec11" style="font-size: 80%;">A simple example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs013.html#___sec12" style="font-size: 80%;">Back to the more realistic cases</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs014.html#___sec13" style="font-size: 80%;">Summary of course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs015.html#___sec14" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs016.html#___sec15" style="font-size: 80%;">Topics we have covered this year</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs017.html#___sec16" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs018.html#___sec17" style="font-size: 80%;">Machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs019.html#___sec18" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs020.html#___sec19" style="font-size: 80%;">Perspective on Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs021.html#___sec20" style="font-size: 80%;">Machine Learning Research</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs022.html#___sec21" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs023.html#___sec22" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs024.html#___sec23" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs025.html#___sec24" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs026.html#___sec25" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs027.html#___sec26" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- 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>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs029.html#___sec28" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs030.html#___sec29" style="font-size: 80%;">What's the future like?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs031.html#___sec30" style="font-size: 80%;">Types of Machine Learning, a repetition</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs032.html#___sec31" style="font-size: 80%;">Why Boltzmann machines?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs033.html#___sec32" style="font-size: 80%;">Boltzmann Machines</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs034.html#___sec33" style="font-size: 80%;">Some similarities and differences from DNNs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs035.html#___sec34" style="font-size: 80%;">Boltzmann machines (BM)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs036.html#___sec35" style="font-size: 80%;">A standard BM setup</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs037.html#___sec36" style="font-size: 80%;">The structure of the RBM network</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs038.html#___sec37" style="font-size: 80%;">The network</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs039.html#___sec38" style="font-size: 80%;">Goals</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs040.html#___sec39" style="font-size: 80%;">Joint distribution</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs041.html#___sec40" style="font-size: 80%;">Network Elements, the energy function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs042.html#___sec41" style="font-size: 80%;">Defining different types of RBMs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs043.html#___sec42" style="font-size: 80%;">More about RBMs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs044.html#___sec43" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs045.html#___sec44" style="font-size: 80%;">Bayesian Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs046.html#___sec45" style="font-size: 80%;">Reinforcement Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs047.html#___sec46" style="font-size: 80%;">Transfer learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs048.html#___sec47" style="font-size: 80%;">Adversarial learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs049.html#___sec48" style="font-size: 80%;">Dual learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs050.html#___sec49" style="font-size: 80%;">Distributed machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs051.html#___sec50" style="font-size: 80%;">Meta learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs052.html#___sec51" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs053.html#___sec52" style="font-size: 80%;">Explainable machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs054.html#___sec53" style="font-size: 80%;">Quantum machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec54" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs056.html#___sec55" style="font-size: 80%;">Quantum reinforcement learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs057.html#___sec56" style="font-size: 80%;">Quantum deep learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs058.html#___sec57" style="font-size: 80%;">Social machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs059.html#___sec58" style="font-size: 80%;">The last words?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs060.html#___sec59" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
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</div>
|
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|
||||
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||||
|
||||
<a name="part0055"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec54" class="anchor">Quantum machine learning algorithms based on linear algebra </h2>
|
||||
|
||||
<p>
|
||||
Many quantum machine learning algorithms are based on variants of
|
||||
quantum algorithms for solving linear equations, which can efficiently
|
||||
solve N-variable linear equations with complexity of O(log2 N) under
|
||||
certain conditions. The quantum matrix inversion algorithm can
|
||||
accelerate many machine learning methods, such as least square linear
|
||||
regression, least square version of support vector machine, Gaussian
|
||||
process, and more. The training of these algorithms can be simplified
|
||||
to solve linear equations. The key bottleneck of this type of quantum
|
||||
machine learning algorithms is data input—that is, how to initialize
|
||||
the quantum system with the entire data set. Although efficient
|
||||
data-input algorithms exist for certain situations, how to efficiently
|
||||
input data into a quantum system is as yet unknown for most cases.
|
||||
|
||||
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|
||||
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<li><a href="._week48-bs054.html">«</a></li>
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<li><a href="._week48-bs047.html">48</a></li>
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||||
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|
||||
('Thursday', 2, None, '___sec1'),
|
||||
('Friday', 2, None, '___sec2'),
|
||||
('Support Vector Machines, overarching aims', 2, None, '___sec3'),
|
||||
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|
||||
('The equations', 2, None, '___sec5'),
|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
('What? Me worry? No final exam in this course!',
|
||||
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|
||||
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|
||||
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|
||||
('Topics we have covered this year', 2, None, '___sec15'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('Learning outcomes and overarching aims of this course',
|
||||
2,
|
||||
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|
||||
'___sec18'),
|
||||
('Perspective on Machine Learning', 2, None, '___sec19'),
|
||||
('Machine Learning Research', 2, None, '___sec20'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec21'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec22'),
|
||||
('Preparing Your Data', 2, None, '___sec23'),
|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
||||
2,
|
||||
None,
|
||||
'___sec24'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec25'),
|
||||
('Resampling', 2, None, '___sec26'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
||||
'___sec27'),
|
||||
('Additional courses of interest', 2, None, '___sec28'),
|
||||
("What's the future like?", 2, None, '___sec29'),
|
||||
('Types of Machine Learning, a repetition', 2, None, '___sec30'),
|
||||
('Why Boltzmann machines?', 2, None, '___sec31'),
|
||||
('Boltzmann Machines', 2, None, '___sec32'),
|
||||
('Some similarities and differences from DNNs',
|
||||
2,
|
||||
None,
|
||||
'___sec33'),
|
||||
('Boltzmann machines (BM)', 2, None, '___sec34'),
|
||||
('A standard BM setup', 2, None, '___sec35'),
|
||||
('The structure of the RBM network', 2, None, '___sec36'),
|
||||
('The network', 2, None, '___sec37'),
|
||||
('Goals', 2, None, '___sec38'),
|
||||
('Joint distribution', 2, None, '___sec39'),
|
||||
('Network Elements, the energy function', 2, None, '___sec40'),
|
||||
('Defining different types of RBMs', 2, None, '___sec41'),
|
||||
('More about RBMs', 2, None, '___sec42'),
|
||||
('Autoencoders: Overarching view', 2, None, '___sec43'),
|
||||
('Bayesian Machine Learning', 2, None, '___sec44'),
|
||||
('Reinforcement Learning', 2, None, '___sec45'),
|
||||
('Transfer learning', 2, None, '___sec46'),
|
||||
('Adversarial learning', 2, None, '___sec47'),
|
||||
('Dual learning', 2, None, '___sec48'),
|
||||
('Distributed machine learning', 2, None, '___sec49'),
|
||||
('Meta learning', 2, None, '___sec50'),
|
||||
('The Challenges Facing Machine Learning', 2, None, '___sec51'),
|
||||
('Explainable machine learning', 2, None, '___sec52'),
|
||||
('Quantum machine learning', 2, None, '___sec53'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'___sec54'),
|
||||
('Quantum reinforcement learning', 2, None, '___sec55'),
|
||||
('Quantum deep learning', 2, None, '___sec56'),
|
||||
('Social machine learning', 2, None, '___sec57'),
|
||||
('The last words?', 2, None, '___sec58'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'___sec59')]}
|
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end of tocinfo -->
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<!-- navigation toc: --> <li><a href="._week48-bs001.html#___sec0" style="font-size: 80%;">Overview of week 48</a></li>
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||||
<!-- 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%;">Different kernels and Mercer's theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs009.html#___sec8" style="font-size: 80%;">The moons example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs010.html#___sec9" style="font-size: 80%;">Mathematical optimization of convex functions</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs011.html#___sec10" style="font-size: 80%;">How do we solve these problems?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs012.html#___sec11" style="font-size: 80%;">A simple example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs013.html#___sec12" style="font-size: 80%;">Back to the more realistic cases</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs014.html#___sec13" style="font-size: 80%;">Summary of course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs015.html#___sec14" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs016.html#___sec15" style="font-size: 80%;">Topics we have covered this year</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs017.html#___sec16" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs018.html#___sec17" style="font-size: 80%;">Machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs019.html#___sec18" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs020.html#___sec19" style="font-size: 80%;">Perspective on Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs021.html#___sec20" style="font-size: 80%;">Machine Learning Research</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs022.html#___sec21" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs023.html#___sec22" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs024.html#___sec23" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs025.html#___sec24" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs026.html#___sec25" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs027.html#___sec26" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- 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>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs029.html#___sec28" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs030.html#___sec29" style="font-size: 80%;">What's the future like?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs031.html#___sec30" style="font-size: 80%;">Types of Machine Learning, a repetition</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs032.html#___sec31" style="font-size: 80%;">Why Boltzmann machines?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs033.html#___sec32" style="font-size: 80%;">Boltzmann Machines</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs034.html#___sec33" style="font-size: 80%;">Some similarities and differences from DNNs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs035.html#___sec34" style="font-size: 80%;">Boltzmann machines (BM)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs036.html#___sec35" style="font-size: 80%;">A standard BM setup</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs037.html#___sec36" style="font-size: 80%;">The structure of the RBM network</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs038.html#___sec37" style="font-size: 80%;">The network</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs039.html#___sec38" style="font-size: 80%;">Goals</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs040.html#___sec39" style="font-size: 80%;">Joint distribution</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs041.html#___sec40" style="font-size: 80%;">Network Elements, the energy function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs042.html#___sec41" style="font-size: 80%;">Defining different types of RBMs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs043.html#___sec42" style="font-size: 80%;">More about RBMs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs044.html#___sec43" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs045.html#___sec44" style="font-size: 80%;">Bayesian Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs046.html#___sec45" style="font-size: 80%;">Reinforcement Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs047.html#___sec46" style="font-size: 80%;">Transfer learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs048.html#___sec47" style="font-size: 80%;">Adversarial learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs049.html#___sec48" style="font-size: 80%;">Dual learning</a></li>
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||||
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||||
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||||
<!-- navigation toc: --> <li><a href="._week48-bs052.html#___sec51" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs053.html#___sec52" style="font-size: 80%;">Explainable machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs054.html#___sec53" style="font-size: 80%;">Quantum machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs055.html#___sec54" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec55" style="font-size: 80%;">Quantum reinforcement learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs057.html#___sec56" style="font-size: 80%;">Quantum deep learning</a></li>
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||||
<!-- navigation toc: --> <li><a href="._week48-bs058.html#___sec57" style="font-size: 80%;">Social machine learning</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs059.html#___sec58" style="font-size: 80%;">The last words?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs060.html#___sec59" 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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|
||||
|
||||
<h2 id="___sec55" class="anchor">Quantum reinforcement learning </h2>
|
||||
|
||||
<p>
|
||||
In quantum reinforcement learning, a quantum agent interacts with the
|
||||
classical environment to obtain rewards from the environment, so as to
|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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||||
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<!-- navigation toc: --> <li><a href="._week48-bs001.html#___sec0" style="font-size: 80%;">Overview of week 48</a></li>
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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||||
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||||
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<h2 id="___sec56" class="anchor">Quantum deep learning </h2>
|
||||
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
correspond to an adjustable Ising model. Then, by initializing the
|
||||
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|
||||
the system to heat up, we can read out the output qubits to get the
|
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result.
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<!-- navigation toc: --> <li><a href="._week48-bs001.html#___sec0" style="font-size: 80%;">Overview of week 48</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-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>
|
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<!-- 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%;">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>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs010.html#___sec9" style="font-size: 80%;">Mathematical optimization of convex functions</a></li>
|
||||
<!-- 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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|
||||
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|
||||
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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>
|
||||
<!-- 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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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
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|
||||
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<!-- navigation toc: --> <li><a href="._week48-bs060.html#___sec59" 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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|
||||
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||||
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|
||||
Machine learning aims to imitate how humans
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
evolve by imitating human society so as to achieve more effective,
|
||||
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|
||||
|
||||
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And much more.
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<!-- navigation toc: --> <li><a href="._week48-bs001.html#___sec0" style="font-size: 80%;">Overview of week 48</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-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%;">Different kernels and Mercer's theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs009.html#___sec8" style="font-size: 80%;">The moons example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs010.html#___sec9" style="font-size: 80%;">Mathematical optimization of convex functions</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs011.html#___sec10" style="font-size: 80%;">How do we solve these problems?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs012.html#___sec11" style="font-size: 80%;">A simple example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs013.html#___sec12" style="font-size: 80%;">Back to the more realistic cases</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs014.html#___sec13" style="font-size: 80%;">Summary of course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs015.html#___sec14" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs016.html#___sec15" style="font-size: 80%;">Topics we have covered this year</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs017.html#___sec16" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs018.html#___sec17" style="font-size: 80%;">Machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs019.html#___sec18" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs020.html#___sec19" style="font-size: 80%;">Perspective on Machine Learning</a></li>
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
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<!-- navigation toc: --> <li><a href="._week48-bs001.html#___sec0" style="font-size: 80%;">Overview of week 48</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-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%;">Different kernels and Mercer's theorem</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs009.html#___sec8" style="font-size: 80%;">The moons example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs010.html#___sec9" style="font-size: 80%;">Mathematical optimization of convex functions</a></li>
|
||||
<!-- 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>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs013.html#___sec12" style="font-size: 80%;">Back to the more realistic cases</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs014.html#___sec13" style="font-size: 80%;">Summary of course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs015.html#___sec14" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs016.html#___sec15" style="font-size: 80%;">Topics we have covered this year</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs017.html#___sec16" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs018.html#___sec17" style="font-size: 80%;">Machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs019.html#___sec18" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs020.html#___sec19" style="font-size: 80%;">Perspective on Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs021.html#___sec20" style="font-size: 80%;">Machine Learning Research</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs022.html#___sec21" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs023.html#___sec22" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs024.html#___sec23" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs025.html#___sec24" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs026.html#___sec25" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs027.html#___sec26" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- 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>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs029.html#___sec28" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs030.html#___sec29" style="font-size: 80%;">What's the future like?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs031.html#___sec30" style="font-size: 80%;">Types of Machine Learning, a repetition</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs032.html#___sec31" style="font-size: 80%;">Why Boltzmann machines?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs033.html#___sec32" style="font-size: 80%;">Boltzmann Machines</a></li>
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||||
<!-- navigation toc: --> <li><a href="._week48-bs034.html#___sec33" style="font-size: 80%;">Some similarities and differences from DNNs</a></li>
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||||
<!-- navigation toc: --> <li><a href="._week48-bs035.html#___sec34" style="font-size: 80%;">Boltzmann machines (BM)</a></li>
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||||
<!-- navigation toc: --> <li><a href="._week48-bs036.html#___sec35" style="font-size: 80%;">A standard BM setup</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs037.html#___sec36" style="font-size: 80%;">The structure of the RBM network</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs038.html#___sec37" style="font-size: 80%;">The network</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs039.html#___sec38" style="font-size: 80%;">Goals</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs040.html#___sec39" style="font-size: 80%;">Joint distribution</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs041.html#___sec40" style="font-size: 80%;">Network Elements, the energy function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs042.html#___sec41" style="font-size: 80%;">Defining different types of RBMs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs043.html#___sec42" style="font-size: 80%;">More about RBMs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs044.html#___sec43" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs045.html#___sec44" style="font-size: 80%;">Bayesian Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs046.html#___sec45" style="font-size: 80%;">Reinforcement Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs047.html#___sec46" style="font-size: 80%;">Transfer learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs048.html#___sec47" style="font-size: 80%;">Adversarial learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs049.html#___sec48" style="font-size: 80%;">Dual learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs050.html#___sec49" style="font-size: 80%;">Distributed machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs051.html#___sec50" style="font-size: 80%;">Meta learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs052.html#___sec51" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs053.html#___sec52" style="font-size: 80%;">Explainable machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs054.html#___sec53" style="font-size: 80%;">Quantum machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs055.html#___sec54" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs056.html#___sec55" style="font-size: 80%;">Quantum reinforcement learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs057.html#___sec56" style="font-size: 80%;">Quantum deep learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs058.html#___sec57" style="font-size: 80%;">Social machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week48-bs059.html#___sec58" style="font-size: 80%;">The last words?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec59" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
|
||||
|
||||
</ul>
|
||||
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|
||||
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||||
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||||
<h2 id="___sec59" 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 />
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