added figs
This commit is contained in:
Morten Hjorth-Jensen
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<a class="navbar-brand" href="week47-bs.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods 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="._week47-bs001.html#plan-for-week-47" style="font-size: 80%;">Plan for week 47</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs002.html#bagging" style="font-size: 80%;">Bagging</a></li>
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||||
<!-- navigation toc: --> <li><a href="._week47-bs003.html#more-bagging" style="font-size: 80%;">More bagging</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs004.html#making-your-own-bootstrap-changing-the-level-of-the-decision-tree" style="font-size: 80%;">Making your own Bootstrap: Changing the Level of the Decision Tree</a></li>
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||||
<!-- navigation toc: --> <li><a href="._week47-bs005.html#random-forests" style="font-size: 80%;">Random forests</a></li>
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||||
<!-- navigation toc: --> <li><a href="._week47-bs006.html#random-forest-algorithm" style="font-size: 80%;">Random Forest Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs007.html#random-forests-compared-with-other-methods-on-the-cancer-data" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs008.html#compare-bagging-on-trees-with-random-forests" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
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||||
<!-- navigation toc: --> <li><a href="._week47-bs009.html#boosting-a-bird-s-eye-view" style="font-size: 80%;">Boosting, a Bird's Eye View</a></li>
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||||
<!-- navigation toc: --> <li><a href="._week47-bs010.html#what-is-boosting-additive-modelling-iterative-fitting" style="font-size: 80%;">What is boosting? Additive Modelling/Iterative Fitting</a></li>
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||||
<!-- navigation toc: --> <li><a href="._week47-bs011.html#iterative-fitting-regression-and-squared-error-cost-function" style="font-size: 80%;">Iterative Fitting, Regression and Squared-error Cost Function</a></li>
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||||
<!-- navigation toc: --> <li><a href="._week47-bs012.html#squared-error-example-and-iterative-fitting" style="font-size: 80%;">Squared-Error Example and Iterative Fitting</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs013.html#iterative-fitting-classification-and-adaboost" style="font-size: 80%;">Iterative Fitting, Classification and AdaBoost</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs014.html#adaptive-boosting-adaboost" style="font-size: 80%;">Adaptive Boosting, AdaBoost</a></li>
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||||
<!-- navigation toc: --> <li><a href="._week47-bs015.html#building-up-adaboost" style="font-size: 80%;">Building up AdaBoost</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs016.html#adaptive-boosting-adaboost-basic-algorithm" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
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||||
<!-- navigation toc: --> <li><a href="._week47-bs017.html#basic-steps-of-adaboost" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
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||||
<!-- navigation toc: --> <li><a href="._week47-bs018.html#adaboost-examples" style="font-size: 80%;">AdaBoost Examples</a></li>
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||||
<!-- navigation toc: --> <li><a href="._week47-bs019.html#gradient-boosting-basics-with-steepest-descent-functional-gradient-descent" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs020.html#the-squared-error-again-steepest-descent" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs021.html#steepest-descent-example" style="font-size: 80%;">Steepest Descent Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs022.html#gradient-boosting-algorithm" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs023.html#gradient-boosting-examples-of-regression" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs024.html#gradient-boosting-classification-example" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs025.html#xgboost-extreme-gradient-boosting" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs026.html#regression-case" style="font-size: 80%;">Regression Case</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs027.html#xgboost-on-the-cancer-data" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs028.html#summary-of-course" style="font-size: 80%;">Summary of course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs029.html#what-me-worry-no-final-exam-in-this-course" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks" style="font-size: 80%;">What is the link between Artificial Intelligence and Machine Learning and some general Remarks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs031.html#going-back-to-the-beginning-of-the-semester" style="font-size: 80%;">Going back to the beginning of the semester</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs032.html#not-so-sharp-distinctions" style="font-size: 80%;">Not so sharp distinctions</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs033.html#topics-we-have-covered-this-year" style="font-size: 80%;">Topics we have covered this year</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs034.html#statistical-analysis-and-optimization-of-data" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs035.html#machine-learning" style="font-size: 80%;">Machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs036.html#learning-outcomes-and-overarching-aims-of-this-course" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs037.html#perspective-on-machine-learning" style="font-size: 80%;">Perspective on Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs038.html#machine-learning-research" style="font-size: 80%;">Machine Learning Research</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs039.html#starting-your-machine-learning-project" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs040.html#choose-a-model-and-algorithm" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs041.html#preparing-your-data" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs042.html#which-activation-and-weights-to-choose-in-neural-networks" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs043.html#optimization-methods-and-hyperparameters" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs044.html#resampling" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs045.html#other-courses-on-data-science-and-machine-learning-at-uio" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs046.html#additional-courses-of-interest" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs047.html#what-s-the-future-like" style="font-size: 80%;">What's the future like?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs048.html#types-of-machine-learning-a-repetition" style="font-size: 80%;">Types of Machine Learning, a repetition</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs049.html#why-boltzmann-machines" style="font-size: 80%;">Why Boltzmann machines?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs050.html#boltzmann-machines" style="font-size: 80%;">Boltzmann Machines</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs051.html#some-similarities-and-differences-from-dnns" style="font-size: 80%;">Some similarities and differences from DNNs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs052.html#boltzmann-machines-bm" style="font-size: 80%;">Boltzmann machines (BM)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs053.html#a-standard-bm-setup" style="font-size: 80%;">A standard BM setup</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs054.html#the-structure-of-the-rbm-network" style="font-size: 80%;">The structure of the RBM network</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs055.html#the-network" style="font-size: 80%;">The network</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs056.html#goals" style="font-size: 80%;">Goals</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs057.html#joint-distribution" style="font-size: 80%;">Joint distribution</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs058.html#network-elements-the-energy-function" style="font-size: 80%;">Network Elements, the energy function</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs059.html#defining-different-types-of-rbms" style="font-size: 80%;">Defining different types of RBMs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs060.html#more-about-rbms" style="font-size: 80%;">More about RBMs</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs061.html#autoencoders-overarching-view" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs062.html#bayesian-machine-learning" style="font-size: 80%;">Bayesian Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs063.html#reinforcement-learning" style="font-size: 80%;">Reinforcement Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs064.html#transfer-learning" style="font-size: 80%;">Transfer learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs065.html#adversarial-learning" style="font-size: 80%;">Adversarial learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs066.html#dual-learning" style="font-size: 80%;">Dual learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs067.html#distributed-machine-learning" style="font-size: 80%;">Distributed machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs068.html#meta-learning" style="font-size: 80%;">Meta learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs069.html#the-challenges-facing-machine-learning" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs070.html#explainable-machine-learning" style="font-size: 80%;">Explainable machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs071.html#scientific-machine-learning" style="font-size: 80%;">Scientific Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs072.html#quantum-machine-learning" style="font-size: 80%;">Quantum machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs073.html#quantum-machine-learning-algorithms-based-on-linear-algebra" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs074.html#quantum-reinforcement-learning" style="font-size: 80%;">Quantum reinforcement learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs075.html#quantum-deep-learning" style="font-size: 80%;">Quantum deep learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs076.html#social-machine-learning" style="font-size: 80%;">Social machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs077.html#the-last-words" style="font-size: 80%;">The last words?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs078.html#ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean" style="font-size: 80%;">AI/ML and some statements you may have heard (and what do they mean?)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester" 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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|
||||
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||||
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||||
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||||
<!-- !split -->
|
||||
<h2 id="best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester" class="anchor">Best wishes to you all and thanks so much for your heroic efforts this semester </h2>
|
||||
|
||||
<br/><br/>
|
||||
<center>
|
||||
<p><img src="figures/Nebbdyr2.png" width="500" align="bottom"></p>
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|
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|
||||
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<li><a href="">...</a></li>
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<li><a href="._week47-bs071.html">72</a></li>
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<li><a href="._week47-bs072.html">73</a></li>
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<li><a href="._week47-bs077.html">78</a></li>
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<li><a href="._week47-bs078.html">79</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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|
||||
('Diagonalize the sample covariance matrix to obtain 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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|
||||
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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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||||
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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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|
||||
2,
|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
'mean?)',
|
||||
2,
|
||||
None,
|
||||
'ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester')]}
|
||||
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<a class="navbar-brand" href="week47-bs.html">Week 47: Unsupervised learning (PCA and Clustering) and Summary of Course</a>
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<!-- navigation toc: --> <li><a href="._week47-bs001.html#overview-of-week-47" style="font-size: 80%;"><b>Overview of week 47</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs002.html#basic-ideas-of-the-principal-component-analysis-pca" style="font-size: 80%;"><b>Basic ideas of the Principal Component Analysis (PCA)</b></a></li>
|
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<!-- navigation toc: --> <li><a href="._week47-bs003.html#introducing-the-covariance-and-correlation-functions" style="font-size: 80%;"><b>Introducing the Covariance and Correlation functions</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs004.html#more-on-the-covariance" style="font-size: 80%;"><b>More on the covariance</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs005.html#reminding-ourselves-about-linear-regression" style="font-size: 80%;"><b>Reminding ourselves about Linear Regression</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs006.html#simple-example" style="font-size: 80%;"><b>Simple Example</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs007.html#the-correlation-matrix" style="font-size: 80%;"><b>The Correlation Matrix</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs008.html#numpy-functionality" style="font-size: 80%;"><b>Numpy Functionality</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs009.html#correlation-matrix-again" style="font-size: 80%;"><b>Correlation Matrix again</b></a></li>
|
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<!-- navigation toc: --> <li><a href="._week47-bs010.html#using-pandas" style="font-size: 80%;"><b>Using Pandas</b></a></li>
|
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<!-- navigation toc: --> <li><a href="._week47-bs011.html#and-then-the-franke-function" style="font-size: 80%;"><b>And then the Franke Function</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs012.html#links-with-the-design-matrix" style="font-size: 80%;"><b>Links with the Design Matrix</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs013.html#computing-the-expectation-values" style="font-size: 80%;"><b>Computing the Expectation Values</b></a></li>
|
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<!-- navigation toc: --> <li><a href="._week47-bs014.html#towards-the-pca-theorem" style="font-size: 80%;"><b>Towards the PCA theorem</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs015.html#more-on-the-pca-theorem" style="font-size: 80%;"><b>More on the PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs016.html#a-kind-of-bird-s-view-on-pca" style="font-size: 80%;"><b>A kind of Bird's view on PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs017.html#writing-our-own-pca-code" style="font-size: 80%;"><b>Writing our own PCA code</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs018.html#implementing-it" style="font-size: 80%;"><b>Implementing it</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs019.html#first-step" style="font-size: 80%;"><b>First Step</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs020.html#scaling" style="font-size: 80%;"><b>Scaling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs021.html#centered-data" style="font-size: 80%;"><b>Centered Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs022.html#exploring" style="font-size: 80%;"><b>Exploring</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs023.html#diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components" style="font-size: 80%;"><b>Diagonalize the sample covariance matrix to obtain the principal components</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs024.html#collecting-all-steps" style="font-size: 80%;"><b>Collecting all Steps</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs025.html#classical-pca-theorem" style="font-size: 80%;"><b>Classical PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs026.html#the-pca-theorem" style="font-size: 80%;"><b>The PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs027.html#geometric-interpretation-and-link-with-singular-value-decomposition" style="font-size: 80%;"><b>Geometric Interpretation and link with Singular Value Decomposition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs028.html#pca-and-scikit-learn" style="font-size: 80%;"><b>PCA and scikit-learn</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs029.html#back-to-the-cancer-data" style="font-size: 80%;"><b>Back to the Cancer Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#incremental-pca" style="font-size: 80%;"><b>Incremental PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#randomized-pca" style="font-size: 80%;"> Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#kernel-pca" style="font-size: 80%;"> Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs031.html#other-techniques" style="font-size: 80%;"><b>Other techniques</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs032.html#clustering-and-unsupervised-learning" style="font-size: 80%;"><b>Clustering and Unsupervised Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs033.html#basic-idea-of-the-k-means-clustering-algorithm" style="font-size: 80%;"><b>Basic Idea of the \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs034.html#the-k-means-algorithm" style="font-size: 80%;"><b>The \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs035.html#basic-math-of-the-k-means-algorithm" style="font-size: 80%;"><b>Basic Math of the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs036.html#within-cluster-point-scatter" style="font-size: 80%;"><b>Within Cluster Point Scatter</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs037.html#more-details" style="font-size: 80%;"><b>More Details</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs038.html#total-cluster-variance" style="font-size: 80%;"><b>Total Cluster Variance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs039.html#the-k-means-clustering-algorithm" style="font-size: 80%;"><b>The \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs040.html#summarizing" style="font-size: 80%;"><b>Summarizing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs041.html#writing-our-own-code-the-data-set" style="font-size: 80%;"><b>Writing our own Code, the Data Set</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs042.html#implementing-the-k-means-algorithm" style="font-size: 80%;"><b>Implementing the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs043.html#plotting" style="font-size: 80%;"><b>Plotting</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs044.html#continuing" style="font-size: 80%;"><b>Continuing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs045.html#wrapping-it-up" style="font-size: 80%;"><b>Wrapping it up</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs046.html#summary-of-course" style="font-size: 80%;"><b>Summary of course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs047.html#what-me-worry-no-final-exam-in-this-course" style="font-size: 80%;"><b>What? Me worry? No final exam in this course!</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs048.html#what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks" style="font-size: 80%;"><b>What is the link between Artificial Intelligence and Machine Learning and some general Remarks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs049.html#going-back-to-the-beginning-of-the-semester" style="font-size: 80%;"><b>Going back to the beginning of the semester</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs050.html#not-so-sharp-distinctions" style="font-size: 80%;"><b>Not so sharp distinctions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs051.html#topics-we-have-covered-this-year" style="font-size: 80%;"><b>Topics we have covered this year</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs052.html#statistical-analysis-and-optimization-of-data" style="font-size: 80%;"><b>Statistical analysis and optimization of data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs053.html#machine-learning" style="font-size: 80%;"><b>Machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs054.html#learning-outcomes-and-overarching-aims-of-this-course" style="font-size: 80%;"><b>Learning outcomes and overarching aims of this course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs055.html#perspective-on-machine-learning" style="font-size: 80%;"><b>Perspective on Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs056.html#machine-learning-research" style="font-size: 80%;"><b>Machine Learning Research</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs057.html#starting-your-machine-learning-project" style="font-size: 80%;"><b>Starting your Machine Learning Project</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs058.html#choose-a-model-and-algorithm" style="font-size: 80%;"><b>Choose a Model and Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs059.html#preparing-your-data" style="font-size: 80%;"><b>Preparing Your Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs060.html#which-activation-and-weights-to-choose-in-neural-networks" style="font-size: 80%;"><b>Which Activation and Weights to Choose in Neural Networks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs061.html#optimization-methods-and-hyperparameters" style="font-size: 80%;"><b>Optimization Methods and Hyperparameters</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs062.html#resampling" style="font-size: 80%;"><b>Resampling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs063.html#other-courses-on-data-science-and-machine-learning-at-uio" style="font-size: 80%;"><b>Other courses on Data science and Machine Learning at UiO</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs064.html#additional-courses-of-interest" style="font-size: 80%;"><b>Additional courses of interest</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs065.html#what-s-the-future-like" style="font-size: 80%;"><b>What's the future like?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs066.html#types-of-machine-learning-a-repetition" style="font-size: 80%;"><b>Types of Machine Learning, a repetition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs067.html#why-boltzmann-machines" style="font-size: 80%;"><b>Why Boltzmann machines?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs068.html#boltzmann-machines" style="font-size: 80%;"><b>Boltzmann Machines</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs069.html#some-similarities-and-differences-from-dnns" style="font-size: 80%;"><b>Some similarities and differences from DNNs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs070.html#boltzmann-machines-bm" style="font-size: 80%;"><b>Boltzmann machines (BM)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs071.html#a-standard-bm-setup" style="font-size: 80%;"><b>A standard BM setup</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs072.html#the-structure-of-the-rbm-network" style="font-size: 80%;"><b>The structure of the RBM network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs073.html#the-network" style="font-size: 80%;"><b>The network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs074.html#goals" style="font-size: 80%;"><b>Goals</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs075.html#joint-distribution" style="font-size: 80%;"><b>Joint distribution</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs076.html#network-elements-the-energy-function" style="font-size: 80%;"><b>Network Elements, the energy function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs077.html#defining-different-types-of-rbms" style="font-size: 80%;"><b>Defining different types of RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs078.html#more-about-rbms" style="font-size: 80%;"><b>More about RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs079.html#autoencoders-overarching-view" style="font-size: 80%;"><b>Autoencoders: Overarching view</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="#bayesian-machine-learning" style="font-size: 80%;"><b>Bayesian Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs081.html#reinforcement-learning" style="font-size: 80%;"><b>Reinforcement Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs082.html#transfer-learning" style="font-size: 80%;"><b>Transfer learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs083.html#adversarial-learning" style="font-size: 80%;"><b>Adversarial learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs084.html#dual-learning" style="font-size: 80%;"><b>Dual learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs085.html#distributed-machine-learning" style="font-size: 80%;"><b>Distributed machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs086.html#meta-learning" style="font-size: 80%;"><b>Meta learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs087.html#the-challenges-facing-machine-learning" style="font-size: 80%;"><b>The Challenges Facing Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs088.html#explainable-machine-learning" style="font-size: 80%;"><b>Explainable machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs089.html#scientific-machine-learning" style="font-size: 80%;"><b>Scientific Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs090.html#quantum-machine-learning" style="font-size: 80%;"><b>Quantum machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs091.html#quantum-machine-learning-algorithms-based-on-linear-algebra" style="font-size: 80%;"><b>Quantum machine learning algorithms based on linear algebra</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs092.html#quantum-reinforcement-learning" style="font-size: 80%;"><b>Quantum reinforcement learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs093.html#quantum-deep-learning" style="font-size: 80%;"><b>Quantum deep learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs094.html#social-machine-learning" style="font-size: 80%;"><b>Social machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs095.html#the-last-words" style="font-size: 80%;"><b>The last words?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs096.html#ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean" style="font-size: 80%;"><b>AI/ML and some statements you may have heard (and what do they mean?)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs097.html#best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester" style="font-size: 80%;"><b>Best wishes to you all and thanks so much for your heroic efforts this semester</b></a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
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</div>
|
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|
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</div> <!-- end of navigation bar -->
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<div class="container">
|
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<p> </p><p> </p><p> </p> <!-- add vertical space -->
|
||||
<a name="part0080"></a>
|
||||
<!-- !split -->
|
||||
<h2 id="bayesian-machine-learning" class="anchor">Bayesian Machine Learning </h2>
|
||||
|
||||
<p>This is an important topic if we aim at extracting a probability
|
||||
distribution. This gives us also a confidence interval and error
|
||||
estimates.
|
||||
</p>
|
||||
|
||||
<p>Bayesian machine learning allows us to encode our prior beliefs about
|
||||
what those models should look like, independent of what the data tells
|
||||
us. This is especially useful when we don’t have a ton of data to
|
||||
confidently learn our model.
|
||||
</p>
|
||||
|
||||
<a href="https://www.youtube.com/watch?v=E1qhGw8QxqY&ab_channel=AndrewGordonWilson" target="_self">Video on Bayesian deep learning</a>
|
||||
|
||||
<p>See also the <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Articles/lec03.pdf" target="_self">slides here</a>.</p>
|
||||
|
||||
<p>
|
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<li><a href="._week47-bs088.html">89</a></li>
|
||||
<li><a href="._week47-bs089.html">90</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._week47-bs097.html">98</a></li>
|
||||
<li><a href="._week47-bs081.html">»</a></li>
|
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|
||||
('Basic ideas of the Principal Component Analysis (PCA)',
|
||||
2,
|
||||
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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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||||
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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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|
||||
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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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|
||||
2,
|
||||
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|
||||
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|
||||
('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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<a class="navbar-brand" href="week47-bs.html">Week 47: Unsupervised learning (PCA and Clustering) and Summary of Course</a>
|
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|
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|
||||
<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
|
||||
<ul class="dropdown-menu">
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs001.html#overview-of-week-47" style="font-size: 80%;"><b>Overview of week 47</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs002.html#basic-ideas-of-the-principal-component-analysis-pca" style="font-size: 80%;"><b>Basic ideas of the Principal Component Analysis (PCA)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs003.html#introducing-the-covariance-and-correlation-functions" style="font-size: 80%;"><b>Introducing the Covariance and Correlation functions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs004.html#more-on-the-covariance" style="font-size: 80%;"><b>More on the covariance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs005.html#reminding-ourselves-about-linear-regression" style="font-size: 80%;"><b>Reminding ourselves about Linear Regression</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs006.html#simple-example" style="font-size: 80%;"><b>Simple Example</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs007.html#the-correlation-matrix" style="font-size: 80%;"><b>The Correlation Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs008.html#numpy-functionality" style="font-size: 80%;"><b>Numpy Functionality</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs009.html#correlation-matrix-again" style="font-size: 80%;"><b>Correlation Matrix again</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs010.html#using-pandas" style="font-size: 80%;"><b>Using Pandas</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs011.html#and-then-the-franke-function" style="font-size: 80%;"><b>And then the Franke Function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs012.html#links-with-the-design-matrix" style="font-size: 80%;"><b>Links with the Design Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs013.html#computing-the-expectation-values" style="font-size: 80%;"><b>Computing the Expectation Values</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs014.html#towards-the-pca-theorem" style="font-size: 80%;"><b>Towards the PCA theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs015.html#more-on-the-pca-theorem" style="font-size: 80%;"><b>More on the PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs016.html#a-kind-of-bird-s-view-on-pca" style="font-size: 80%;"><b>A kind of Bird's view on PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs017.html#writing-our-own-pca-code" style="font-size: 80%;"><b>Writing our own PCA code</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs018.html#implementing-it" style="font-size: 80%;"><b>Implementing it</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs019.html#first-step" style="font-size: 80%;"><b>First Step</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs020.html#scaling" style="font-size: 80%;"><b>Scaling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs021.html#centered-data" style="font-size: 80%;"><b>Centered Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs022.html#exploring" style="font-size: 80%;"><b>Exploring</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs023.html#diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components" style="font-size: 80%;"><b>Diagonalize the sample covariance matrix to obtain the principal components</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs024.html#collecting-all-steps" style="font-size: 80%;"><b>Collecting all Steps</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs025.html#classical-pca-theorem" style="font-size: 80%;"><b>Classical PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs026.html#the-pca-theorem" style="font-size: 80%;"><b>The PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs027.html#geometric-interpretation-and-link-with-singular-value-decomposition" style="font-size: 80%;"><b>Geometric Interpretation and link with Singular Value Decomposition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs028.html#pca-and-scikit-learn" style="font-size: 80%;"><b>PCA and scikit-learn</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs029.html#back-to-the-cancer-data" style="font-size: 80%;"><b>Back to the Cancer Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#incremental-pca" style="font-size: 80%;"><b>Incremental PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#randomized-pca" style="font-size: 80%;"> Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#kernel-pca" style="font-size: 80%;"> Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs031.html#other-techniques" style="font-size: 80%;"><b>Other techniques</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs032.html#clustering-and-unsupervised-learning" style="font-size: 80%;"><b>Clustering and Unsupervised Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs033.html#basic-idea-of-the-k-means-clustering-algorithm" style="font-size: 80%;"><b>Basic Idea of the \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs034.html#the-k-means-algorithm" style="font-size: 80%;"><b>The \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs035.html#basic-math-of-the-k-means-algorithm" style="font-size: 80%;"><b>Basic Math of the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs036.html#within-cluster-point-scatter" style="font-size: 80%;"><b>Within Cluster Point Scatter</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs037.html#more-details" style="font-size: 80%;"><b>More Details</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs038.html#total-cluster-variance" style="font-size: 80%;"><b>Total Cluster Variance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs039.html#the-k-means-clustering-algorithm" style="font-size: 80%;"><b>The \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs040.html#summarizing" style="font-size: 80%;"><b>Summarizing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs041.html#writing-our-own-code-the-data-set" style="font-size: 80%;"><b>Writing our own Code, the Data Set</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs042.html#implementing-the-k-means-algorithm" style="font-size: 80%;"><b>Implementing the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs043.html#plotting" style="font-size: 80%;"><b>Plotting</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs044.html#continuing" style="font-size: 80%;"><b>Continuing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs045.html#wrapping-it-up" style="font-size: 80%;"><b>Wrapping it up</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs046.html#summary-of-course" style="font-size: 80%;"><b>Summary of course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs047.html#what-me-worry-no-final-exam-in-this-course" style="font-size: 80%;"><b>What? Me worry? No final exam in this course!</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs048.html#what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks" style="font-size: 80%;"><b>What is the link between Artificial Intelligence and Machine Learning and some general Remarks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs049.html#going-back-to-the-beginning-of-the-semester" style="font-size: 80%;"><b>Going back to the beginning of the semester</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs050.html#not-so-sharp-distinctions" style="font-size: 80%;"><b>Not so sharp distinctions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs051.html#topics-we-have-covered-this-year" style="font-size: 80%;"><b>Topics we have covered this year</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs052.html#statistical-analysis-and-optimization-of-data" style="font-size: 80%;"><b>Statistical analysis and optimization of data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs053.html#machine-learning" style="font-size: 80%;"><b>Machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs054.html#learning-outcomes-and-overarching-aims-of-this-course" style="font-size: 80%;"><b>Learning outcomes and overarching aims of this course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs055.html#perspective-on-machine-learning" style="font-size: 80%;"><b>Perspective on Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs056.html#machine-learning-research" style="font-size: 80%;"><b>Machine Learning Research</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs057.html#starting-your-machine-learning-project" style="font-size: 80%;"><b>Starting your Machine Learning Project</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs058.html#choose-a-model-and-algorithm" style="font-size: 80%;"><b>Choose a Model and Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs059.html#preparing-your-data" style="font-size: 80%;"><b>Preparing Your Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs060.html#which-activation-and-weights-to-choose-in-neural-networks" style="font-size: 80%;"><b>Which Activation and Weights to Choose in Neural Networks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs061.html#optimization-methods-and-hyperparameters" style="font-size: 80%;"><b>Optimization Methods and Hyperparameters</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs062.html#resampling" style="font-size: 80%;"><b>Resampling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs063.html#other-courses-on-data-science-and-machine-learning-at-uio" style="font-size: 80%;"><b>Other courses on Data science and Machine Learning at UiO</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs064.html#additional-courses-of-interest" style="font-size: 80%;"><b>Additional courses of interest</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs065.html#what-s-the-future-like" style="font-size: 80%;"><b>What's the future like?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs066.html#types-of-machine-learning-a-repetition" style="font-size: 80%;"><b>Types of Machine Learning, a repetition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs067.html#why-boltzmann-machines" style="font-size: 80%;"><b>Why Boltzmann machines?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs068.html#boltzmann-machines" style="font-size: 80%;"><b>Boltzmann Machines</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs069.html#some-similarities-and-differences-from-dnns" style="font-size: 80%;"><b>Some similarities and differences from DNNs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs070.html#boltzmann-machines-bm" style="font-size: 80%;"><b>Boltzmann machines (BM)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs071.html#a-standard-bm-setup" style="font-size: 80%;"><b>A standard BM setup</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs072.html#the-structure-of-the-rbm-network" style="font-size: 80%;"><b>The structure of the RBM network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs073.html#the-network" style="font-size: 80%;"><b>The network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs074.html#goals" style="font-size: 80%;"><b>Goals</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs075.html#joint-distribution" style="font-size: 80%;"><b>Joint distribution</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs076.html#network-elements-the-energy-function" style="font-size: 80%;"><b>Network Elements, the energy function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs077.html#defining-different-types-of-rbms" style="font-size: 80%;"><b>Defining different types of RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs078.html#more-about-rbms" style="font-size: 80%;"><b>More about RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs079.html#autoencoders-overarching-view" style="font-size: 80%;"><b>Autoencoders: Overarching view</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs080.html#bayesian-machine-learning" style="font-size: 80%;"><b>Bayesian Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="#reinforcement-learning" style="font-size: 80%;"><b>Reinforcement Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs082.html#transfer-learning" style="font-size: 80%;"><b>Transfer learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs083.html#adversarial-learning" style="font-size: 80%;"><b>Adversarial learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs084.html#dual-learning" style="font-size: 80%;"><b>Dual learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs085.html#distributed-machine-learning" style="font-size: 80%;"><b>Distributed machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs086.html#meta-learning" style="font-size: 80%;"><b>Meta learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs087.html#the-challenges-facing-machine-learning" style="font-size: 80%;"><b>The Challenges Facing Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs088.html#explainable-machine-learning" style="font-size: 80%;"><b>Explainable machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs089.html#scientific-machine-learning" style="font-size: 80%;"><b>Scientific Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs090.html#quantum-machine-learning" style="font-size: 80%;"><b>Quantum machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs091.html#quantum-machine-learning-algorithms-based-on-linear-algebra" style="font-size: 80%;"><b>Quantum machine learning algorithms based on linear algebra</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs092.html#quantum-reinforcement-learning" style="font-size: 80%;"><b>Quantum reinforcement learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs093.html#quantum-deep-learning" style="font-size: 80%;"><b>Quantum deep learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs094.html#social-machine-learning" style="font-size: 80%;"><b>Social machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs095.html#the-last-words" style="font-size: 80%;"><b>The last words?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs096.html#ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean" style="font-size: 80%;"><b>AI/ML and some statements you may have heard (and what do they mean?)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs097.html#best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester" style="font-size: 80%;"><b>Best wishes to you all and thanks so much for your heroic efforts this semester</b></a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
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||||
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|
||||
<p> </p><p> </p><p> </p> <!-- add vertical space -->
|
||||
<a name="part0081"></a>
|
||||
<!-- !split -->
|
||||
<h2 id="reinforcement-learning" class="anchor">Reinforcement Learning </h2>
|
||||
|
||||
<p>Reinforcement Learning (RL) is one of the most exciting fields of
|
||||
Machine Learning today, and also one of the oldest. It has been around
|
||||
since the 1950s, producing many interesting applications over the
|
||||
years.
|
||||
</p>
|
||||
|
||||
<p>It studies
|
||||
how agents take actions based on trial and error, so as to maximize
|
||||
some notion of cumulative reward in a dynamic system or
|
||||
environment. Due to its generality, the problem has also been studied
|
||||
in many other disciplines, such as game theory, control theory,
|
||||
operations research, information theory, multi-agent systems, swarm
|
||||
intelligence, statistics, and genetic algorithms.
|
||||
</p>
|
||||
|
||||
<p>In March 2016, AlphaGo, a computer program that plays the board game
|
||||
Go, beat Lee Sedol in a five-game match. This was the first time a
|
||||
computer Go program had beaten a 9-dan (highest rank) professional
|
||||
without handicaps. AlphaGo is based on deep convolutional neural
|
||||
networks and reinforcement learning. AlphaGo’s victory was a major
|
||||
milestone in artificial intelligence and it has also made
|
||||
reinforcement learning a hot research area in the field of machine
|
||||
learning.
|
||||
</p>
|
||||
|
||||
<p><a href="https://www.youtube.com/watch?v=FgzM3zpZ55o&ab_channel=stanfordonline" target="_self">Lecture on Reinforcement Learning</a>.</p>
|
||||
|
||||
<p>See also A. Geron's textbook, chapter 16.</p>
|
||||
|
||||
<p>
|
||||
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|
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|
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|
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|
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|
||||
('Randomized PCA', 3, None, 'randomized-pca'),
|
||||
('Kernel PCA', 3, None, 'kernel-pca'),
|
||||
('Other techniques', 2, None, 'other-techniques'),
|
||||
('Clustering and Unsupervised Learning',
|
||||
2,
|
||||
None,
|
||||
'clustering-and-unsupervised-learning'),
|
||||
('Basic Idea of the $k$-means Clustering Algorithm',
|
||||
2,
|
||||
None,
|
||||
'basic-idea-of-the-k-means-clustering-algorithm'),
|
||||
('The $k$-means Algorithm', 2, None, 'the-k-means-algorithm'),
|
||||
('Basic Math of the $k$-means Algorithm',
|
||||
2,
|
||||
None,
|
||||
'basic-math-of-the-k-means-algorithm'),
|
||||
('Within Cluster Point Scatter',
|
||||
2,
|
||||
None,
|
||||
'within-cluster-point-scatter'),
|
||||
('More Details', 2, None, 'more-details'),
|
||||
('Total Cluster Variance', 2, None, 'total-cluster-variance'),
|
||||
('The $k$-means Clustering Algorithm',
|
||||
2,
|
||||
None,
|
||||
'the-k-means-clustering-algorithm'),
|
||||
('Summarizing', 2, None, 'summarizing'),
|
||||
('Writing our own Code, the Data Set',
|
||||
2,
|
||||
None,
|
||||
'writing-our-own-code-the-data-set'),
|
||||
('Implementing the $k$-means Algorithm',
|
||||
2,
|
||||
None,
|
||||
'implementing-the-k-means-algorithm'),
|
||||
('Plotting', 2, None, 'plotting'),
|
||||
('Continuing', 2, None, 'continuing'),
|
||||
('Wrapping it up', 2, None, 'wrapping-it-up'),
|
||||
('Summary of course', 2, None, 'summary-of-course'),
|
||||
('What? Me worry? No final exam in this course!',
|
||||
2,
|
||||
None,
|
||||
'what-me-worry-no-final-exam-in-this-course'),
|
||||
('What is the link between Artificial Intelligence and Machine '
|
||||
'Learning and some general Remarks',
|
||||
2,
|
||||
None,
|
||||
'what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks'),
|
||||
('Going back to the beginning of the semester',
|
||||
2,
|
||||
None,
|
||||
'going-back-to-the-beginning-of-the-semester'),
|
||||
('Not so sharp distinctions',
|
||||
2,
|
||||
None,
|
||||
'not-so-sharp-distinctions'),
|
||||
('Topics we have covered this year',
|
||||
2,
|
||||
None,
|
||||
'topics-we-have-covered-this-year'),
|
||||
('Statistical analysis and optimization of data',
|
||||
2,
|
||||
None,
|
||||
'statistical-analysis-and-optimization-of-data'),
|
||||
('Machine learning', 2, None, 'machine-learning'),
|
||||
('Learning outcomes and overarching aims of this course',
|
||||
2,
|
||||
None,
|
||||
'learning-outcomes-and-overarching-aims-of-this-course'),
|
||||
('Perspective on Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'perspective-on-machine-learning'),
|
||||
('Machine Learning Research',
|
||||
2,
|
||||
None,
|
||||
'machine-learning-research'),
|
||||
('Starting your Machine Learning Project',
|
||||
2,
|
||||
None,
|
||||
'starting-your-machine-learning-project'),
|
||||
('Choose a Model and Algorithm',
|
||||
2,
|
||||
None,
|
||||
'choose-a-model-and-algorithm'),
|
||||
('Preparing Your Data', 2, None, 'preparing-your-data'),
|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
||||
2,
|
||||
None,
|
||||
'which-activation-and-weights-to-choose-in-neural-networks'),
|
||||
('Optimization Methods and Hyperparameters',
|
||||
2,
|
||||
None,
|
||||
'optimization-methods-and-hyperparameters'),
|
||||
('Resampling', 2, None, 'resampling'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
||||
'other-courses-on-data-science-and-machine-learning-at-uio'),
|
||||
('Additional courses of interest',
|
||||
2,
|
||||
None,
|
||||
'additional-courses-of-interest'),
|
||||
("What's the future like?", 2, None, 'what-s-the-future-like'),
|
||||
('Types of Machine Learning, a repetition',
|
||||
2,
|
||||
None,
|
||||
'types-of-machine-learning-a-repetition'),
|
||||
('Why Boltzmann machines?', 2, None, 'why-boltzmann-machines'),
|
||||
('Boltzmann Machines', 2, None, 'boltzmann-machines'),
|
||||
('Some similarities and differences from DNNs',
|
||||
2,
|
||||
None,
|
||||
'some-similarities-and-differences-from-dnns'),
|
||||
('Boltzmann machines (BM)', 2, None, 'boltzmann-machines-bm'),
|
||||
('A standard BM setup', 2, None, 'a-standard-bm-setup'),
|
||||
('The structure of the RBM network',
|
||||
2,
|
||||
None,
|
||||
'the-structure-of-the-rbm-network'),
|
||||
('The network', 2, None, 'the-network'),
|
||||
('Goals', 2, None, 'goals'),
|
||||
('Joint distribution', 2, None, 'joint-distribution'),
|
||||
('Network Elements, the energy function',
|
||||
2,
|
||||
None,
|
||||
'network-elements-the-energy-function'),
|
||||
('Defining different types of RBMs',
|
||||
2,
|
||||
None,
|
||||
'defining-different-types-of-rbms'),
|
||||
('More about RBMs', 2, None, 'more-about-rbms'),
|
||||
('Autoencoders: Overarching view',
|
||||
2,
|
||||
None,
|
||||
'autoencoders-overarching-view'),
|
||||
('Bayesian Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'bayesian-machine-learning'),
|
||||
('Reinforcement Learning', 2, None, 'reinforcement-learning'),
|
||||
('Transfer learning', 2, None, 'transfer-learning'),
|
||||
('Adversarial learning', 2, None, 'adversarial-learning'),
|
||||
('Dual learning', 2, None, 'dual-learning'),
|
||||
('Distributed machine learning',
|
||||
2,
|
||||
None,
|
||||
'distributed-machine-learning'),
|
||||
('Meta learning', 2, None, 'meta-learning'),
|
||||
('The Challenges Facing Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'the-challenges-facing-machine-learning'),
|
||||
('Explainable machine learning',
|
||||
2,
|
||||
None,
|
||||
'explainable-machine-learning'),
|
||||
('Scientific Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'scientific-machine-learning'),
|
||||
('Quantum machine learning', 2, None, 'quantum-machine-learning'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'quantum-machine-learning-algorithms-based-on-linear-algebra'),
|
||||
('Quantum reinforcement learning',
|
||||
2,
|
||||
None,
|
||||
'quantum-reinforcement-learning'),
|
||||
('Quantum deep learning', 2, None, 'quantum-deep-learning'),
|
||||
('Social machine learning', 2, None, 'social-machine-learning'),
|
||||
('The last words?', 2, None, 'the-last-words'),
|
||||
('AI/ML and some statements you may have heard (and what do they '
|
||||
'mean?)',
|
||||
2,
|
||||
None,
|
||||
'ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester')]}
|
||||
end of tocinfo -->
|
||||
|
||||
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|
||||
|
||||
|
||||
|
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|
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<a class="navbar-brand" href="week47-bs.html">Week 47: Unsupervised learning (PCA and Clustering) and Summary of Course</a>
|
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|
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|
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<ul class="nav navbar-nav navbar-right">
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|
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<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
|
||||
<ul class="dropdown-menu">
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs001.html#overview-of-week-47" style="font-size: 80%;"><b>Overview of week 47</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs002.html#basic-ideas-of-the-principal-component-analysis-pca" style="font-size: 80%;"><b>Basic ideas of the Principal Component Analysis (PCA)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs003.html#introducing-the-covariance-and-correlation-functions" style="font-size: 80%;"><b>Introducing the Covariance and Correlation functions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs004.html#more-on-the-covariance" style="font-size: 80%;"><b>More on the covariance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs005.html#reminding-ourselves-about-linear-regression" style="font-size: 80%;"><b>Reminding ourselves about Linear Regression</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs006.html#simple-example" style="font-size: 80%;"><b>Simple Example</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs007.html#the-correlation-matrix" style="font-size: 80%;"><b>The Correlation Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs008.html#numpy-functionality" style="font-size: 80%;"><b>Numpy Functionality</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs009.html#correlation-matrix-again" style="font-size: 80%;"><b>Correlation Matrix again</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs010.html#using-pandas" style="font-size: 80%;"><b>Using Pandas</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs011.html#and-then-the-franke-function" style="font-size: 80%;"><b>And then the Franke Function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs012.html#links-with-the-design-matrix" style="font-size: 80%;"><b>Links with the Design Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs013.html#computing-the-expectation-values" style="font-size: 80%;"><b>Computing the Expectation Values</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs014.html#towards-the-pca-theorem" style="font-size: 80%;"><b>Towards the PCA theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs015.html#more-on-the-pca-theorem" style="font-size: 80%;"><b>More on the PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs016.html#a-kind-of-bird-s-view-on-pca" style="font-size: 80%;"><b>A kind of Bird's view on PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs017.html#writing-our-own-pca-code" style="font-size: 80%;"><b>Writing our own PCA code</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs018.html#implementing-it" style="font-size: 80%;"><b>Implementing it</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs019.html#first-step" style="font-size: 80%;"><b>First Step</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs020.html#scaling" style="font-size: 80%;"><b>Scaling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs021.html#centered-data" style="font-size: 80%;"><b>Centered Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs022.html#exploring" style="font-size: 80%;"><b>Exploring</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs023.html#diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components" style="font-size: 80%;"><b>Diagonalize the sample covariance matrix to obtain the principal components</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs024.html#collecting-all-steps" style="font-size: 80%;"><b>Collecting all Steps</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs025.html#classical-pca-theorem" style="font-size: 80%;"><b>Classical PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs026.html#the-pca-theorem" style="font-size: 80%;"><b>The PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs027.html#geometric-interpretation-and-link-with-singular-value-decomposition" style="font-size: 80%;"><b>Geometric Interpretation and link with Singular Value Decomposition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs028.html#pca-and-scikit-learn" style="font-size: 80%;"><b>PCA and scikit-learn</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs029.html#back-to-the-cancer-data" style="font-size: 80%;"><b>Back to the Cancer Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#incremental-pca" style="font-size: 80%;"><b>Incremental PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#randomized-pca" style="font-size: 80%;"> Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#kernel-pca" style="font-size: 80%;"> Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs031.html#other-techniques" style="font-size: 80%;"><b>Other techniques</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs032.html#clustering-and-unsupervised-learning" style="font-size: 80%;"><b>Clustering and Unsupervised Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs033.html#basic-idea-of-the-k-means-clustering-algorithm" style="font-size: 80%;"><b>Basic Idea of the \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs034.html#the-k-means-algorithm" style="font-size: 80%;"><b>The \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs035.html#basic-math-of-the-k-means-algorithm" style="font-size: 80%;"><b>Basic Math of the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs036.html#within-cluster-point-scatter" style="font-size: 80%;"><b>Within Cluster Point Scatter</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs037.html#more-details" style="font-size: 80%;"><b>More Details</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs038.html#total-cluster-variance" style="font-size: 80%;"><b>Total Cluster Variance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs039.html#the-k-means-clustering-algorithm" style="font-size: 80%;"><b>The \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs040.html#summarizing" style="font-size: 80%;"><b>Summarizing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs041.html#writing-our-own-code-the-data-set" style="font-size: 80%;"><b>Writing our own Code, the Data Set</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs042.html#implementing-the-k-means-algorithm" style="font-size: 80%;"><b>Implementing the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs043.html#plotting" style="font-size: 80%;"><b>Plotting</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs044.html#continuing" style="font-size: 80%;"><b>Continuing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs045.html#wrapping-it-up" style="font-size: 80%;"><b>Wrapping it up</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs046.html#summary-of-course" style="font-size: 80%;"><b>Summary of course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs047.html#what-me-worry-no-final-exam-in-this-course" style="font-size: 80%;"><b>What? Me worry? No final exam in this course!</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs048.html#what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks" style="font-size: 80%;"><b>What is the link between Artificial Intelligence and Machine Learning and some general Remarks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs049.html#going-back-to-the-beginning-of-the-semester" style="font-size: 80%;"><b>Going back to the beginning of the semester</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs050.html#not-so-sharp-distinctions" style="font-size: 80%;"><b>Not so sharp distinctions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs051.html#topics-we-have-covered-this-year" style="font-size: 80%;"><b>Topics we have covered this year</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs052.html#statistical-analysis-and-optimization-of-data" style="font-size: 80%;"><b>Statistical analysis and optimization of data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs053.html#machine-learning" style="font-size: 80%;"><b>Machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs054.html#learning-outcomes-and-overarching-aims-of-this-course" style="font-size: 80%;"><b>Learning outcomes and overarching aims of this course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs055.html#perspective-on-machine-learning" style="font-size: 80%;"><b>Perspective on Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs056.html#machine-learning-research" style="font-size: 80%;"><b>Machine Learning Research</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs057.html#starting-your-machine-learning-project" style="font-size: 80%;"><b>Starting your Machine Learning Project</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs058.html#choose-a-model-and-algorithm" style="font-size: 80%;"><b>Choose a Model and Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs059.html#preparing-your-data" style="font-size: 80%;"><b>Preparing Your Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs060.html#which-activation-and-weights-to-choose-in-neural-networks" style="font-size: 80%;"><b>Which Activation and Weights to Choose in Neural Networks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs061.html#optimization-methods-and-hyperparameters" style="font-size: 80%;"><b>Optimization Methods and Hyperparameters</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs062.html#resampling" style="font-size: 80%;"><b>Resampling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs063.html#other-courses-on-data-science-and-machine-learning-at-uio" style="font-size: 80%;"><b>Other courses on Data science and Machine Learning at UiO</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs064.html#additional-courses-of-interest" style="font-size: 80%;"><b>Additional courses of interest</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs065.html#what-s-the-future-like" style="font-size: 80%;"><b>What's the future like?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs066.html#types-of-machine-learning-a-repetition" style="font-size: 80%;"><b>Types of Machine Learning, a repetition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs067.html#why-boltzmann-machines" style="font-size: 80%;"><b>Why Boltzmann machines?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs068.html#boltzmann-machines" style="font-size: 80%;"><b>Boltzmann Machines</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs069.html#some-similarities-and-differences-from-dnns" style="font-size: 80%;"><b>Some similarities and differences from DNNs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs070.html#boltzmann-machines-bm" style="font-size: 80%;"><b>Boltzmann machines (BM)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs071.html#a-standard-bm-setup" style="font-size: 80%;"><b>A standard BM setup</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs072.html#the-structure-of-the-rbm-network" style="font-size: 80%;"><b>The structure of the RBM network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs073.html#the-network" style="font-size: 80%;"><b>The network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs074.html#goals" style="font-size: 80%;"><b>Goals</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs075.html#joint-distribution" style="font-size: 80%;"><b>Joint distribution</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs076.html#network-elements-the-energy-function" style="font-size: 80%;"><b>Network Elements, the energy function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs077.html#defining-different-types-of-rbms" style="font-size: 80%;"><b>Defining different types of RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs078.html#more-about-rbms" style="font-size: 80%;"><b>More about RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs079.html#autoencoders-overarching-view" style="font-size: 80%;"><b>Autoencoders: Overarching view</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs080.html#bayesian-machine-learning" style="font-size: 80%;"><b>Bayesian Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs081.html#reinforcement-learning" style="font-size: 80%;"><b>Reinforcement Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="#transfer-learning" style="font-size: 80%;"><b>Transfer learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs083.html#adversarial-learning" style="font-size: 80%;"><b>Adversarial learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs084.html#dual-learning" style="font-size: 80%;"><b>Dual learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs085.html#distributed-machine-learning" style="font-size: 80%;"><b>Distributed machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs086.html#meta-learning" style="font-size: 80%;"><b>Meta learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs087.html#the-challenges-facing-machine-learning" style="font-size: 80%;"><b>The Challenges Facing Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs088.html#explainable-machine-learning" style="font-size: 80%;"><b>Explainable machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs089.html#scientific-machine-learning" style="font-size: 80%;"><b>Scientific Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs090.html#quantum-machine-learning" style="font-size: 80%;"><b>Quantum machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs091.html#quantum-machine-learning-algorithms-based-on-linear-algebra" style="font-size: 80%;"><b>Quantum machine learning algorithms based on linear algebra</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs092.html#quantum-reinforcement-learning" style="font-size: 80%;"><b>Quantum reinforcement learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs093.html#quantum-deep-learning" style="font-size: 80%;"><b>Quantum deep learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs094.html#social-machine-learning" style="font-size: 80%;"><b>Social machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs095.html#the-last-words" style="font-size: 80%;"><b>The last words?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs096.html#ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean" style="font-size: 80%;"><b>AI/ML and some statements you may have heard (and what do they mean?)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs097.html#best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester" style="font-size: 80%;"><b>Best wishes to you all and thanks so much for your heroic efforts this semester</b></a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<p> </p><p> </p><p> </p> <!-- add vertical space -->
|
||||
<a name="part0082"></a>
|
||||
<!-- !split -->
|
||||
<h2 id="transfer-learning" class="anchor">Transfer learning </h2>
|
||||
|
||||
<p>The goal of transfer learning is to transfer the model or knowledge
|
||||
obtained from a source task to the target task, in order to resolve
|
||||
the issues of insufficient training data in the target task. The
|
||||
rationality of doing so lies in that usually the source and target
|
||||
tasks have inter-correlations, and therefore either the features,
|
||||
samples, or models in the source task might provide useful information
|
||||
for us to better solve the target task. Transfer learning is a hot
|
||||
research topic in recent years, with many problems still waiting to be studied.
|
||||
</p>
|
||||
|
||||
<p><a href="https://www.ias.edu/video/machinelearning/2020/0331-SamoryKpotufe" target="_self">Lecture on transfer learning</a>.</p>
|
||||
|
||||
<p>
|
||||
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|
||||
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|
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|
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|
||||
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|
||||
('Basic ideas of the Principal Component Analysis (PCA)',
|
||||
2,
|
||||
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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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|
||||
("A kind of Bird's view on PCA",
|
||||
2,
|
||||
None,
|
||||
'a-kind-of-bird-s-view-on-pca'),
|
||||
('Writing our own PCA code', 2, None, 'writing-our-own-pca-code'),
|
||||
('Implementing it', 2, None, 'implementing-it'),
|
||||
('First Step', 2, None, 'first-step'),
|
||||
('Scaling', 2, None, 'scaling'),
|
||||
('Centered Data', 2, None, 'centered-data'),
|
||||
('Exploring', 2, None, 'exploring'),
|
||||
('Diagonalize the sample covariance matrix to obtain the '
|
||||
'principal components',
|
||||
2,
|
||||
None,
|
||||
'diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components'),
|
||||
('Collecting all Steps', 2, None, 'collecting-all-steps'),
|
||||
('Classical PCA Theorem', 2, None, 'classical-pca-theorem'),
|
||||
('The PCA Theorem', 2, None, 'the-pca-theorem'),
|
||||
('Geometric Interpretation and link with Singular Value '
|
||||
'Decomposition',
|
||||
2,
|
||||
None,
|
||||
'geometric-interpretation-and-link-with-singular-value-decomposition'),
|
||||
('PCA and scikit-learn', 2, None, 'pca-and-scikit-learn'),
|
||||
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|
||||
('Incremental PCA', 2, None, 'incremental-pca'),
|
||||
('Randomized PCA', 3, None, 'randomized-pca'),
|
||||
('Kernel PCA', 3, None, 'kernel-pca'),
|
||||
('Other techniques', 2, None, 'other-techniques'),
|
||||
('Clustering and Unsupervised Learning',
|
||||
2,
|
||||
None,
|
||||
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|
||||
('Basic Idea of the $k$-means Clustering Algorithm',
|
||||
2,
|
||||
None,
|
||||
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|
||||
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|
||||
('Basic Math of the $k$-means Algorithm',
|
||||
2,
|
||||
None,
|
||||
'basic-math-of-the-k-means-algorithm'),
|
||||
('Within Cluster Point Scatter',
|
||||
2,
|
||||
None,
|
||||
'within-cluster-point-scatter'),
|
||||
('More Details', 2, None, 'more-details'),
|
||||
('Total Cluster Variance', 2, None, 'total-cluster-variance'),
|
||||
('The $k$-means Clustering Algorithm',
|
||||
2,
|
||||
None,
|
||||
'the-k-means-clustering-algorithm'),
|
||||
('Summarizing', 2, None, 'summarizing'),
|
||||
('Writing our own Code, the Data Set',
|
||||
2,
|
||||
None,
|
||||
'writing-our-own-code-the-data-set'),
|
||||
('Implementing the $k$-means Algorithm',
|
||||
2,
|
||||
None,
|
||||
'implementing-the-k-means-algorithm'),
|
||||
('Plotting', 2, None, 'plotting'),
|
||||
('Continuing', 2, None, 'continuing'),
|
||||
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|
||||
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|
||||
('What? Me worry? No final exam in this course!',
|
||||
2,
|
||||
None,
|
||||
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|
||||
('What is the link between Artificial Intelligence and Machine '
|
||||
'Learning and some general Remarks',
|
||||
2,
|
||||
None,
|
||||
'what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks'),
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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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|
||||
2,
|
||||
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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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|
||||
2,
|
||||
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|
||||
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|
||||
('Optimization Methods and Hyperparameters',
|
||||
2,
|
||||
None,
|
||||
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|
||||
('Resampling', 2, None, 'resampling'),
|
||||
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|
||||
2,
|
||||
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|
||||
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|
||||
('Additional courses of interest',
|
||||
2,
|
||||
None,
|
||||
'additional-courses-of-interest'),
|
||||
("What's the future like?", 2, None, 'what-s-the-future-like'),
|
||||
('Types of Machine Learning, a repetition',
|
||||
2,
|
||||
None,
|
||||
'types-of-machine-learning-a-repetition'),
|
||||
('Why Boltzmann machines?', 2, None, 'why-boltzmann-machines'),
|
||||
('Boltzmann Machines', 2, None, 'boltzmann-machines'),
|
||||
('Some similarities and differences from DNNs',
|
||||
2,
|
||||
None,
|
||||
'some-similarities-and-differences-from-dnns'),
|
||||
('Boltzmann machines (BM)', 2, None, 'boltzmann-machines-bm'),
|
||||
('A standard BM setup', 2, None, 'a-standard-bm-setup'),
|
||||
('The structure of the RBM network',
|
||||
2,
|
||||
None,
|
||||
'the-structure-of-the-rbm-network'),
|
||||
('The network', 2, None, 'the-network'),
|
||||
('Goals', 2, None, 'goals'),
|
||||
('Joint distribution', 2, None, 'joint-distribution'),
|
||||
('Network Elements, the energy function',
|
||||
2,
|
||||
None,
|
||||
'network-elements-the-energy-function'),
|
||||
('Defining different types of RBMs',
|
||||
2,
|
||||
None,
|
||||
'defining-different-types-of-rbms'),
|
||||
('More about RBMs', 2, None, 'more-about-rbms'),
|
||||
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|
||||
2,
|
||||
None,
|
||||
'autoencoders-overarching-view'),
|
||||
('Bayesian Machine Learning',
|
||||
2,
|
||||
None,
|
||||
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|
||||
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|
||||
('Transfer learning', 2, None, 'transfer-learning'),
|
||||
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|
||||
('Dual learning', 2, None, 'dual-learning'),
|
||||
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|
||||
2,
|
||||
None,
|
||||
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|
||||
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|
||||
('The Challenges Facing Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'the-challenges-facing-machine-learning'),
|
||||
('Explainable machine learning',
|
||||
2,
|
||||
None,
|
||||
'explainable-machine-learning'),
|
||||
('Scientific Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'scientific-machine-learning'),
|
||||
('Quantum machine learning', 2, None, 'quantum-machine-learning'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'quantum-machine-learning-algorithms-based-on-linear-algebra'),
|
||||
('Quantum reinforcement learning',
|
||||
2,
|
||||
None,
|
||||
'quantum-reinforcement-learning'),
|
||||
('Quantum deep learning', 2, None, 'quantum-deep-learning'),
|
||||
('Social machine learning', 2, None, 'social-machine-learning'),
|
||||
('The last words?', 2, None, 'the-last-words'),
|
||||
('AI/ML and some statements you may have heard (and what do they '
|
||||
'mean?)',
|
||||
2,
|
||||
None,
|
||||
'ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester')]}
|
||||
end of tocinfo -->
|
||||
|
||||
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|
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|
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<a class="navbar-brand" href="week47-bs.html">Week 47: Unsupervised learning (PCA and Clustering) 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="._week47-bs001.html#overview-of-week-47" style="font-size: 80%;"><b>Overview of week 47</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs002.html#basic-ideas-of-the-principal-component-analysis-pca" style="font-size: 80%;"><b>Basic ideas of the Principal Component Analysis (PCA)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs003.html#introducing-the-covariance-and-correlation-functions" style="font-size: 80%;"><b>Introducing the Covariance and Correlation functions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs004.html#more-on-the-covariance" style="font-size: 80%;"><b>More on the covariance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs005.html#reminding-ourselves-about-linear-regression" style="font-size: 80%;"><b>Reminding ourselves about Linear Regression</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs006.html#simple-example" style="font-size: 80%;"><b>Simple Example</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs007.html#the-correlation-matrix" style="font-size: 80%;"><b>The Correlation Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs008.html#numpy-functionality" style="font-size: 80%;"><b>Numpy Functionality</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs009.html#correlation-matrix-again" style="font-size: 80%;"><b>Correlation Matrix again</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs010.html#using-pandas" style="font-size: 80%;"><b>Using Pandas</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs011.html#and-then-the-franke-function" style="font-size: 80%;"><b>And then the Franke Function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs012.html#links-with-the-design-matrix" style="font-size: 80%;"><b>Links with the Design Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs013.html#computing-the-expectation-values" style="font-size: 80%;"><b>Computing the Expectation Values</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs014.html#towards-the-pca-theorem" style="font-size: 80%;"><b>Towards the PCA theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs015.html#more-on-the-pca-theorem" style="font-size: 80%;"><b>More on the PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs016.html#a-kind-of-bird-s-view-on-pca" style="font-size: 80%;"><b>A kind of Bird's view on PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs017.html#writing-our-own-pca-code" style="font-size: 80%;"><b>Writing our own PCA code</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs018.html#implementing-it" style="font-size: 80%;"><b>Implementing it</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs019.html#first-step" style="font-size: 80%;"><b>First Step</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs020.html#scaling" style="font-size: 80%;"><b>Scaling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs021.html#centered-data" style="font-size: 80%;"><b>Centered Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs022.html#exploring" style="font-size: 80%;"><b>Exploring</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs023.html#diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components" style="font-size: 80%;"><b>Diagonalize the sample covariance matrix to obtain the principal components</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs024.html#collecting-all-steps" style="font-size: 80%;"><b>Collecting all Steps</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs025.html#classical-pca-theorem" style="font-size: 80%;"><b>Classical PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs026.html#the-pca-theorem" style="font-size: 80%;"><b>The PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs027.html#geometric-interpretation-and-link-with-singular-value-decomposition" style="font-size: 80%;"><b>Geometric Interpretation and link with Singular Value Decomposition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs028.html#pca-and-scikit-learn" style="font-size: 80%;"><b>PCA and scikit-learn</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs029.html#back-to-the-cancer-data" style="font-size: 80%;"><b>Back to the Cancer Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#incremental-pca" style="font-size: 80%;"><b>Incremental PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#randomized-pca" style="font-size: 80%;"> Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#kernel-pca" style="font-size: 80%;"> Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs031.html#other-techniques" style="font-size: 80%;"><b>Other techniques</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs032.html#clustering-and-unsupervised-learning" style="font-size: 80%;"><b>Clustering and Unsupervised Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs033.html#basic-idea-of-the-k-means-clustering-algorithm" style="font-size: 80%;"><b>Basic Idea of the \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs034.html#the-k-means-algorithm" style="font-size: 80%;"><b>The \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs035.html#basic-math-of-the-k-means-algorithm" style="font-size: 80%;"><b>Basic Math of the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs036.html#within-cluster-point-scatter" style="font-size: 80%;"><b>Within Cluster Point Scatter</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs037.html#more-details" style="font-size: 80%;"><b>More Details</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs038.html#total-cluster-variance" style="font-size: 80%;"><b>Total Cluster Variance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs039.html#the-k-means-clustering-algorithm" style="font-size: 80%;"><b>The \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs040.html#summarizing" style="font-size: 80%;"><b>Summarizing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs041.html#writing-our-own-code-the-data-set" style="font-size: 80%;"><b>Writing our own Code, the Data Set</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs042.html#implementing-the-k-means-algorithm" style="font-size: 80%;"><b>Implementing the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs043.html#plotting" style="font-size: 80%;"><b>Plotting</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs044.html#continuing" style="font-size: 80%;"><b>Continuing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs045.html#wrapping-it-up" style="font-size: 80%;"><b>Wrapping it up</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs046.html#summary-of-course" style="font-size: 80%;"><b>Summary of course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs047.html#what-me-worry-no-final-exam-in-this-course" style="font-size: 80%;"><b>What? Me worry? No final exam in this course!</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs048.html#what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks" style="font-size: 80%;"><b>What is the link between Artificial Intelligence and Machine Learning and some general Remarks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs049.html#going-back-to-the-beginning-of-the-semester" style="font-size: 80%;"><b>Going back to the beginning of the semester</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs050.html#not-so-sharp-distinctions" style="font-size: 80%;"><b>Not so sharp distinctions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs051.html#topics-we-have-covered-this-year" style="font-size: 80%;"><b>Topics we have covered this year</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs052.html#statistical-analysis-and-optimization-of-data" style="font-size: 80%;"><b>Statistical analysis and optimization of data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs053.html#machine-learning" style="font-size: 80%;"><b>Machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs054.html#learning-outcomes-and-overarching-aims-of-this-course" style="font-size: 80%;"><b>Learning outcomes and overarching aims of this course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs055.html#perspective-on-machine-learning" style="font-size: 80%;"><b>Perspective on Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs056.html#machine-learning-research" style="font-size: 80%;"><b>Machine Learning Research</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs057.html#starting-your-machine-learning-project" style="font-size: 80%;"><b>Starting your Machine Learning Project</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs058.html#choose-a-model-and-algorithm" style="font-size: 80%;"><b>Choose a Model and Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs059.html#preparing-your-data" style="font-size: 80%;"><b>Preparing Your Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs060.html#which-activation-and-weights-to-choose-in-neural-networks" style="font-size: 80%;"><b>Which Activation and Weights to Choose in Neural Networks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs061.html#optimization-methods-and-hyperparameters" style="font-size: 80%;"><b>Optimization Methods and Hyperparameters</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs062.html#resampling" style="font-size: 80%;"><b>Resampling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs063.html#other-courses-on-data-science-and-machine-learning-at-uio" style="font-size: 80%;"><b>Other courses on Data science and Machine Learning at UiO</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs064.html#additional-courses-of-interest" style="font-size: 80%;"><b>Additional courses of interest</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs065.html#what-s-the-future-like" style="font-size: 80%;"><b>What's the future like?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs066.html#types-of-machine-learning-a-repetition" style="font-size: 80%;"><b>Types of Machine Learning, a repetition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs067.html#why-boltzmann-machines" style="font-size: 80%;"><b>Why Boltzmann machines?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs068.html#boltzmann-machines" style="font-size: 80%;"><b>Boltzmann Machines</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs069.html#some-similarities-and-differences-from-dnns" style="font-size: 80%;"><b>Some similarities and differences from DNNs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs070.html#boltzmann-machines-bm" style="font-size: 80%;"><b>Boltzmann machines (BM)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs071.html#a-standard-bm-setup" style="font-size: 80%;"><b>A standard BM setup</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs072.html#the-structure-of-the-rbm-network" style="font-size: 80%;"><b>The structure of the RBM network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs073.html#the-network" style="font-size: 80%;"><b>The network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs074.html#goals" style="font-size: 80%;"><b>Goals</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs075.html#joint-distribution" style="font-size: 80%;"><b>Joint distribution</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs076.html#network-elements-the-energy-function" style="font-size: 80%;"><b>Network Elements, the energy function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs077.html#defining-different-types-of-rbms" style="font-size: 80%;"><b>Defining different types of RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs078.html#more-about-rbms" style="font-size: 80%;"><b>More about RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs079.html#autoencoders-overarching-view" style="font-size: 80%;"><b>Autoencoders: Overarching view</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs080.html#bayesian-machine-learning" style="font-size: 80%;"><b>Bayesian Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs081.html#reinforcement-learning" style="font-size: 80%;"><b>Reinforcement Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs082.html#transfer-learning" style="font-size: 80%;"><b>Transfer learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="#adversarial-learning" style="font-size: 80%;"><b>Adversarial learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs084.html#dual-learning" style="font-size: 80%;"><b>Dual learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs085.html#distributed-machine-learning" style="font-size: 80%;"><b>Distributed machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs086.html#meta-learning" style="font-size: 80%;"><b>Meta learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs087.html#the-challenges-facing-machine-learning" style="font-size: 80%;"><b>The Challenges Facing Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs088.html#explainable-machine-learning" style="font-size: 80%;"><b>Explainable machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs089.html#scientific-machine-learning" style="font-size: 80%;"><b>Scientific Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs090.html#quantum-machine-learning" style="font-size: 80%;"><b>Quantum machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs091.html#quantum-machine-learning-algorithms-based-on-linear-algebra" style="font-size: 80%;"><b>Quantum machine learning algorithms based on linear algebra</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs092.html#quantum-reinforcement-learning" style="font-size: 80%;"><b>Quantum reinforcement learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs093.html#quantum-deep-learning" style="font-size: 80%;"><b>Quantum deep learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs094.html#social-machine-learning" style="font-size: 80%;"><b>Social machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs095.html#the-last-words" style="font-size: 80%;"><b>The last words?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs096.html#ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean" style="font-size: 80%;"><b>AI/ML and some statements you may have heard (and what do they mean?)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs097.html#best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester" style="font-size: 80%;"><b>Best wishes to you all and thanks so much for your heroic efforts this semester</b></a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div> <!-- end of navigation bar -->
|
||||
<div class="container">
|
||||
<p> </p><p> </p><p> </p> <!-- add vertical space -->
|
||||
<a name="part0083"></a>
|
||||
<!-- !split -->
|
||||
<h2 id="adversarial-learning" class="anchor">Adversarial learning </h2>
|
||||
|
||||
<p>The conventional deep generative model has a potential problem: the
|
||||
model tends to generate extreme instances to maximize the
|
||||
probabilistic likelihood, which will hurt its performance. Adversarial
|
||||
learning utilizes the adversarial behaviors (e.g., generating
|
||||
adversarial instances or training an adversarial model) to enhance the
|
||||
robustness of the model and improve the quality of the generated
|
||||
data. In recent years, one of the most promising unsupervised learning
|
||||
technologies, generative adversarial networks (GAN), has already been
|
||||
successfully applied to image, speech, and text.
|
||||
</p>
|
||||
|
||||
<p><a href="https://www.youtube.com/watch?v=CIfsB_EYsVI&ab_channel=StanfordUniversitySchoolofEngineering" target="_self">Lecture on adversial learning</a>.</p>
|
||||
|
||||
<p>
|
||||
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<li class="active"><a href="._week47-bs083.html">84</a></li>
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<li><a href="._week47-bs084.html">85</a></li>
|
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<li><a href="._week47-bs085.html">86</a></li>
|
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<li><a href="._week47-bs086.html">87</a></li>
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<li><a href="._week47-bs087.html">88</a></li>
|
||||
<li><a href="._week47-bs088.html">89</a></li>
|
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<li><a href="._week47-bs089.html">90</a></li>
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<li><a href="._week47-bs091.html">92</a></li>
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<li><a href="._week47-bs092.html">93</a></li>
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<li><a href="">...</a></li>
|
||||
<li><a href="._week47-bs097.html">98</a></li>
|
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|
||||
('Basic ideas of the Principal Component Analysis (PCA)',
|
||||
2,
|
||||
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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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||||
("What's the future like?", 2, None, 'what-s-the-future-like'),
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||||
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||||
2,
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||||
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||||
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||||
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||||
('Boltzmann Machines', 2, None, 'boltzmann-machines'),
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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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|
||||
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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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|
||||
2,
|
||||
None,
|
||||
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|
||||
('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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<a class="navbar-brand" href="week47-bs.html">Week 47: Unsupervised learning (PCA and Clustering) and Summary of Course</a>
|
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||||
<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
|
||||
<ul class="dropdown-menu">
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs001.html#overview-of-week-47" style="font-size: 80%;"><b>Overview of week 47</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs002.html#basic-ideas-of-the-principal-component-analysis-pca" style="font-size: 80%;"><b>Basic ideas of the Principal Component Analysis (PCA)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs003.html#introducing-the-covariance-and-correlation-functions" style="font-size: 80%;"><b>Introducing the Covariance and Correlation functions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs004.html#more-on-the-covariance" style="font-size: 80%;"><b>More on the covariance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs005.html#reminding-ourselves-about-linear-regression" style="font-size: 80%;"><b>Reminding ourselves about Linear Regression</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs006.html#simple-example" style="font-size: 80%;"><b>Simple Example</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs007.html#the-correlation-matrix" style="font-size: 80%;"><b>The Correlation Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs008.html#numpy-functionality" style="font-size: 80%;"><b>Numpy Functionality</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs009.html#correlation-matrix-again" style="font-size: 80%;"><b>Correlation Matrix again</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs010.html#using-pandas" style="font-size: 80%;"><b>Using Pandas</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs011.html#and-then-the-franke-function" style="font-size: 80%;"><b>And then the Franke Function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs012.html#links-with-the-design-matrix" style="font-size: 80%;"><b>Links with the Design Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs013.html#computing-the-expectation-values" style="font-size: 80%;"><b>Computing the Expectation Values</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs014.html#towards-the-pca-theorem" style="font-size: 80%;"><b>Towards the PCA theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs015.html#more-on-the-pca-theorem" style="font-size: 80%;"><b>More on the PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs016.html#a-kind-of-bird-s-view-on-pca" style="font-size: 80%;"><b>A kind of Bird's view on PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs017.html#writing-our-own-pca-code" style="font-size: 80%;"><b>Writing our own PCA code</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs018.html#implementing-it" style="font-size: 80%;"><b>Implementing it</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs019.html#first-step" style="font-size: 80%;"><b>First Step</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs020.html#scaling" style="font-size: 80%;"><b>Scaling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs021.html#centered-data" style="font-size: 80%;"><b>Centered Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs022.html#exploring" style="font-size: 80%;"><b>Exploring</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs023.html#diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components" style="font-size: 80%;"><b>Diagonalize the sample covariance matrix to obtain the principal components</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs024.html#collecting-all-steps" style="font-size: 80%;"><b>Collecting all Steps</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs025.html#classical-pca-theorem" style="font-size: 80%;"><b>Classical PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs026.html#the-pca-theorem" style="font-size: 80%;"><b>The PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs027.html#geometric-interpretation-and-link-with-singular-value-decomposition" style="font-size: 80%;"><b>Geometric Interpretation and link with Singular Value Decomposition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs028.html#pca-and-scikit-learn" style="font-size: 80%;"><b>PCA and scikit-learn</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs029.html#back-to-the-cancer-data" style="font-size: 80%;"><b>Back to the Cancer Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#incremental-pca" style="font-size: 80%;"><b>Incremental PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#randomized-pca" style="font-size: 80%;"> Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#kernel-pca" style="font-size: 80%;"> Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs031.html#other-techniques" style="font-size: 80%;"><b>Other techniques</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs032.html#clustering-and-unsupervised-learning" style="font-size: 80%;"><b>Clustering and Unsupervised Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs033.html#basic-idea-of-the-k-means-clustering-algorithm" style="font-size: 80%;"><b>Basic Idea of the \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs034.html#the-k-means-algorithm" style="font-size: 80%;"><b>The \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs035.html#basic-math-of-the-k-means-algorithm" style="font-size: 80%;"><b>Basic Math of the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs036.html#within-cluster-point-scatter" style="font-size: 80%;"><b>Within Cluster Point Scatter</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs037.html#more-details" style="font-size: 80%;"><b>More Details</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs038.html#total-cluster-variance" style="font-size: 80%;"><b>Total Cluster Variance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs039.html#the-k-means-clustering-algorithm" style="font-size: 80%;"><b>The \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs040.html#summarizing" style="font-size: 80%;"><b>Summarizing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs041.html#writing-our-own-code-the-data-set" style="font-size: 80%;"><b>Writing our own Code, the Data Set</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs042.html#implementing-the-k-means-algorithm" style="font-size: 80%;"><b>Implementing the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs043.html#plotting" style="font-size: 80%;"><b>Plotting</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs044.html#continuing" style="font-size: 80%;"><b>Continuing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs045.html#wrapping-it-up" style="font-size: 80%;"><b>Wrapping it up</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs046.html#summary-of-course" style="font-size: 80%;"><b>Summary of course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs047.html#what-me-worry-no-final-exam-in-this-course" style="font-size: 80%;"><b>What? Me worry? No final exam in this course!</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs048.html#what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks" style="font-size: 80%;"><b>What is the link between Artificial Intelligence and Machine Learning and some general Remarks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs049.html#going-back-to-the-beginning-of-the-semester" style="font-size: 80%;"><b>Going back to the beginning of the semester</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs050.html#not-so-sharp-distinctions" style="font-size: 80%;"><b>Not so sharp distinctions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs051.html#topics-we-have-covered-this-year" style="font-size: 80%;"><b>Topics we have covered this year</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs052.html#statistical-analysis-and-optimization-of-data" style="font-size: 80%;"><b>Statistical analysis and optimization of data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs053.html#machine-learning" style="font-size: 80%;"><b>Machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs054.html#learning-outcomes-and-overarching-aims-of-this-course" style="font-size: 80%;"><b>Learning outcomes and overarching aims of this course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs055.html#perspective-on-machine-learning" style="font-size: 80%;"><b>Perspective on Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs056.html#machine-learning-research" style="font-size: 80%;"><b>Machine Learning Research</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs057.html#starting-your-machine-learning-project" style="font-size: 80%;"><b>Starting your Machine Learning Project</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs058.html#choose-a-model-and-algorithm" style="font-size: 80%;"><b>Choose a Model and Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs059.html#preparing-your-data" style="font-size: 80%;"><b>Preparing Your Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs060.html#which-activation-and-weights-to-choose-in-neural-networks" style="font-size: 80%;"><b>Which Activation and Weights to Choose in Neural Networks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs061.html#optimization-methods-and-hyperparameters" style="font-size: 80%;"><b>Optimization Methods and Hyperparameters</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs062.html#resampling" style="font-size: 80%;"><b>Resampling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs063.html#other-courses-on-data-science-and-machine-learning-at-uio" style="font-size: 80%;"><b>Other courses on Data science and Machine Learning at UiO</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs064.html#additional-courses-of-interest" style="font-size: 80%;"><b>Additional courses of interest</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs065.html#what-s-the-future-like" style="font-size: 80%;"><b>What's the future like?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs066.html#types-of-machine-learning-a-repetition" style="font-size: 80%;"><b>Types of Machine Learning, a repetition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs067.html#why-boltzmann-machines" style="font-size: 80%;"><b>Why Boltzmann machines?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs068.html#boltzmann-machines" style="font-size: 80%;"><b>Boltzmann Machines</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs069.html#some-similarities-and-differences-from-dnns" style="font-size: 80%;"><b>Some similarities and differences from DNNs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs070.html#boltzmann-machines-bm" style="font-size: 80%;"><b>Boltzmann machines (BM)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs071.html#a-standard-bm-setup" style="font-size: 80%;"><b>A standard BM setup</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs072.html#the-structure-of-the-rbm-network" style="font-size: 80%;"><b>The structure of the RBM network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs073.html#the-network" style="font-size: 80%;"><b>The network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs074.html#goals" style="font-size: 80%;"><b>Goals</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs075.html#joint-distribution" style="font-size: 80%;"><b>Joint distribution</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs076.html#network-elements-the-energy-function" style="font-size: 80%;"><b>Network Elements, the energy function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs077.html#defining-different-types-of-rbms" style="font-size: 80%;"><b>Defining different types of RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs078.html#more-about-rbms" style="font-size: 80%;"><b>More about RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs079.html#autoencoders-overarching-view" style="font-size: 80%;"><b>Autoencoders: Overarching view</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs080.html#bayesian-machine-learning" style="font-size: 80%;"><b>Bayesian Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs081.html#reinforcement-learning" style="font-size: 80%;"><b>Reinforcement Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs082.html#transfer-learning" style="font-size: 80%;"><b>Transfer learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs083.html#adversarial-learning" style="font-size: 80%;"><b>Adversarial learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="#dual-learning" style="font-size: 80%;"><b>Dual learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs085.html#distributed-machine-learning" style="font-size: 80%;"><b>Distributed machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs086.html#meta-learning" style="font-size: 80%;"><b>Meta learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs087.html#the-challenges-facing-machine-learning" style="font-size: 80%;"><b>The Challenges Facing Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs088.html#explainable-machine-learning" style="font-size: 80%;"><b>Explainable machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs089.html#scientific-machine-learning" style="font-size: 80%;"><b>Scientific Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs090.html#quantum-machine-learning" style="font-size: 80%;"><b>Quantum machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs091.html#quantum-machine-learning-algorithms-based-on-linear-algebra" style="font-size: 80%;"><b>Quantum machine learning algorithms based on linear algebra</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs092.html#quantum-reinforcement-learning" style="font-size: 80%;"><b>Quantum reinforcement learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs093.html#quantum-deep-learning" style="font-size: 80%;"><b>Quantum deep learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs094.html#social-machine-learning" style="font-size: 80%;"><b>Social machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs095.html#the-last-words" style="font-size: 80%;"><b>The last words?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs096.html#ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean" style="font-size: 80%;"><b>AI/ML and some statements you may have heard (and what do they mean?)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs097.html#best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester" style="font-size: 80%;"><b>Best wishes to you all and thanks so much for your heroic efforts this semester</b></a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
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<p> </p><p> </p><p> </p> <!-- add vertical space -->
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<a name="part0084"></a>
|
||||
<!-- !split -->
|
||||
<h2 id="dual-learning" class="anchor">Dual learning </h2>
|
||||
|
||||
<p>Dual learning is a new learning paradigm, the basic idea of which is
|
||||
to use the primal-dual structure between machine learning tasks to
|
||||
obtain effective feedback/regularization, and guide and strengthen the
|
||||
learning process, thus reducing the requirement of large-scale labeled
|
||||
data for deep learning. The idea of dual learning has been applied to
|
||||
many problems in machine learning, including machine translation,
|
||||
image style conversion, question answering and generation, image
|
||||
classification and generation, text classification and generation,
|
||||
image-to-text, and text-to-image.
|
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</p>
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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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|
||||
('Diagonalize the sample covariance matrix to obtain 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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|
||||
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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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|
||||
2,
|
||||
None,
|
||||
'clustering-and-unsupervised-learning'),
|
||||
('Basic Idea of the $k$-means Clustering Algorithm',
|
||||
2,
|
||||
None,
|
||||
'basic-idea-of-the-k-means-clustering-algorithm'),
|
||||
('The $k$-means Algorithm', 2, None, 'the-k-means-algorithm'),
|
||||
('Basic Math of the $k$-means Algorithm',
|
||||
2,
|
||||
None,
|
||||
'basic-math-of-the-k-means-algorithm'),
|
||||
('Within Cluster Point Scatter',
|
||||
2,
|
||||
None,
|
||||
'within-cluster-point-scatter'),
|
||||
('More Details', 2, None, 'more-details'),
|
||||
('Total Cluster Variance', 2, None, 'total-cluster-variance'),
|
||||
('The $k$-means Clustering Algorithm',
|
||||
2,
|
||||
None,
|
||||
'the-k-means-clustering-algorithm'),
|
||||
('Summarizing', 2, None, 'summarizing'),
|
||||
('Writing our own Code, the Data Set',
|
||||
2,
|
||||
None,
|
||||
'writing-our-own-code-the-data-set'),
|
||||
('Implementing the $k$-means Algorithm',
|
||||
2,
|
||||
None,
|
||||
'implementing-the-k-means-algorithm'),
|
||||
('Plotting', 2, None, 'plotting'),
|
||||
('Continuing', 2, None, 'continuing'),
|
||||
('Wrapping it up', 2, None, 'wrapping-it-up'),
|
||||
('Summary of course', 2, None, 'summary-of-course'),
|
||||
('What? Me worry? No final exam in this course!',
|
||||
2,
|
||||
None,
|
||||
'what-me-worry-no-final-exam-in-this-course'),
|
||||
('What is the link between Artificial Intelligence and Machine '
|
||||
'Learning and some general Remarks',
|
||||
2,
|
||||
None,
|
||||
'what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks'),
|
||||
('Going back to the beginning of the semester',
|
||||
2,
|
||||
None,
|
||||
'going-back-to-the-beginning-of-the-semester'),
|
||||
('Not so sharp distinctions',
|
||||
2,
|
||||
None,
|
||||
'not-so-sharp-distinctions'),
|
||||
('Topics we have covered this year',
|
||||
2,
|
||||
None,
|
||||
'topics-we-have-covered-this-year'),
|
||||
('Statistical analysis and optimization of data',
|
||||
2,
|
||||
None,
|
||||
'statistical-analysis-and-optimization-of-data'),
|
||||
('Machine learning', 2, None, 'machine-learning'),
|
||||
('Learning outcomes and overarching aims of this course',
|
||||
2,
|
||||
None,
|
||||
'learning-outcomes-and-overarching-aims-of-this-course'),
|
||||
('Perspective on Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'perspective-on-machine-learning'),
|
||||
('Machine Learning Research',
|
||||
2,
|
||||
None,
|
||||
'machine-learning-research'),
|
||||
('Starting your Machine Learning Project',
|
||||
2,
|
||||
None,
|
||||
'starting-your-machine-learning-project'),
|
||||
('Choose a Model and Algorithm',
|
||||
2,
|
||||
None,
|
||||
'choose-a-model-and-algorithm'),
|
||||
('Preparing Your Data', 2, None, 'preparing-your-data'),
|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
||||
2,
|
||||
None,
|
||||
'which-activation-and-weights-to-choose-in-neural-networks'),
|
||||
('Optimization Methods and Hyperparameters',
|
||||
2,
|
||||
None,
|
||||
'optimization-methods-and-hyperparameters'),
|
||||
('Resampling', 2, None, 'resampling'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
||||
'other-courses-on-data-science-and-machine-learning-at-uio'),
|
||||
('Additional courses of interest',
|
||||
2,
|
||||
None,
|
||||
'additional-courses-of-interest'),
|
||||
("What's the future like?", 2, None, 'what-s-the-future-like'),
|
||||
('Types of Machine Learning, a repetition',
|
||||
2,
|
||||
None,
|
||||
'types-of-machine-learning-a-repetition'),
|
||||
('Why Boltzmann machines?', 2, None, 'why-boltzmann-machines'),
|
||||
('Boltzmann Machines', 2, None, 'boltzmann-machines'),
|
||||
('Some similarities and differences from DNNs',
|
||||
2,
|
||||
None,
|
||||
'some-similarities-and-differences-from-dnns'),
|
||||
('Boltzmann machines (BM)', 2, None, 'boltzmann-machines-bm'),
|
||||
('A standard BM setup', 2, None, 'a-standard-bm-setup'),
|
||||
('The structure of the RBM network',
|
||||
2,
|
||||
None,
|
||||
'the-structure-of-the-rbm-network'),
|
||||
('The network', 2, None, 'the-network'),
|
||||
('Goals', 2, None, 'goals'),
|
||||
('Joint distribution', 2, None, 'joint-distribution'),
|
||||
('Network Elements, the energy function',
|
||||
2,
|
||||
None,
|
||||
'network-elements-the-energy-function'),
|
||||
('Defining different types of RBMs',
|
||||
2,
|
||||
None,
|
||||
'defining-different-types-of-rbms'),
|
||||
('More about RBMs', 2, None, 'more-about-rbms'),
|
||||
('Autoencoders: Overarching view',
|
||||
2,
|
||||
None,
|
||||
'autoencoders-overarching-view'),
|
||||
('Bayesian Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'bayesian-machine-learning'),
|
||||
('Reinforcement Learning', 2, None, 'reinforcement-learning'),
|
||||
('Transfer learning', 2, None, 'transfer-learning'),
|
||||
('Adversarial learning', 2, None, 'adversarial-learning'),
|
||||
('Dual learning', 2, None, 'dual-learning'),
|
||||
('Distributed machine learning',
|
||||
2,
|
||||
None,
|
||||
'distributed-machine-learning'),
|
||||
('Meta learning', 2, None, 'meta-learning'),
|
||||
('The Challenges Facing Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'the-challenges-facing-machine-learning'),
|
||||
('Explainable machine learning',
|
||||
2,
|
||||
None,
|
||||
'explainable-machine-learning'),
|
||||
('Scientific Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'scientific-machine-learning'),
|
||||
('Quantum machine learning', 2, None, 'quantum-machine-learning'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'quantum-machine-learning-algorithms-based-on-linear-algebra'),
|
||||
('Quantum reinforcement learning',
|
||||
2,
|
||||
None,
|
||||
'quantum-reinforcement-learning'),
|
||||
('Quantum deep learning', 2, None, 'quantum-deep-learning'),
|
||||
('Social machine learning', 2, None, 'social-machine-learning'),
|
||||
('The last words?', 2, None, 'the-last-words'),
|
||||
('AI/ML and some statements you may have heard (and what do they '
|
||||
'mean?)',
|
||||
2,
|
||||
None,
|
||||
'ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester')]}
|
||||
end of tocinfo -->
|
||||
|
||||
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|
||||
|
||||
|
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|
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<a class="navbar-brand" href="week47-bs.html">Week 47: Unsupervised learning (PCA and Clustering) and Summary of Course</a>
|
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|
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|
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<ul class="nav navbar-nav navbar-right">
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<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
|
||||
<ul class="dropdown-menu">
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs001.html#overview-of-week-47" style="font-size: 80%;"><b>Overview of week 47</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs002.html#basic-ideas-of-the-principal-component-analysis-pca" style="font-size: 80%;"><b>Basic ideas of the Principal Component Analysis (PCA)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs003.html#introducing-the-covariance-and-correlation-functions" style="font-size: 80%;"><b>Introducing the Covariance and Correlation functions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs004.html#more-on-the-covariance" style="font-size: 80%;"><b>More on the covariance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs005.html#reminding-ourselves-about-linear-regression" style="font-size: 80%;"><b>Reminding ourselves about Linear Regression</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs006.html#simple-example" style="font-size: 80%;"><b>Simple Example</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs007.html#the-correlation-matrix" style="font-size: 80%;"><b>The Correlation Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs008.html#numpy-functionality" style="font-size: 80%;"><b>Numpy Functionality</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs009.html#correlation-matrix-again" style="font-size: 80%;"><b>Correlation Matrix again</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs010.html#using-pandas" style="font-size: 80%;"><b>Using Pandas</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs011.html#and-then-the-franke-function" style="font-size: 80%;"><b>And then the Franke Function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs012.html#links-with-the-design-matrix" style="font-size: 80%;"><b>Links with the Design Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs013.html#computing-the-expectation-values" style="font-size: 80%;"><b>Computing the Expectation Values</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs014.html#towards-the-pca-theorem" style="font-size: 80%;"><b>Towards the PCA theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs015.html#more-on-the-pca-theorem" style="font-size: 80%;"><b>More on the PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs016.html#a-kind-of-bird-s-view-on-pca" style="font-size: 80%;"><b>A kind of Bird's view on PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs017.html#writing-our-own-pca-code" style="font-size: 80%;"><b>Writing our own PCA code</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs018.html#implementing-it" style="font-size: 80%;"><b>Implementing it</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs019.html#first-step" style="font-size: 80%;"><b>First Step</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs020.html#scaling" style="font-size: 80%;"><b>Scaling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs021.html#centered-data" style="font-size: 80%;"><b>Centered Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs022.html#exploring" style="font-size: 80%;"><b>Exploring</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs023.html#diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components" style="font-size: 80%;"><b>Diagonalize the sample covariance matrix to obtain the principal components</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs024.html#collecting-all-steps" style="font-size: 80%;"><b>Collecting all Steps</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs025.html#classical-pca-theorem" style="font-size: 80%;"><b>Classical PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs026.html#the-pca-theorem" style="font-size: 80%;"><b>The PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs027.html#geometric-interpretation-and-link-with-singular-value-decomposition" style="font-size: 80%;"><b>Geometric Interpretation and link with Singular Value Decomposition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs028.html#pca-and-scikit-learn" style="font-size: 80%;"><b>PCA and scikit-learn</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs029.html#back-to-the-cancer-data" style="font-size: 80%;"><b>Back to the Cancer Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#incremental-pca" style="font-size: 80%;"><b>Incremental PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#randomized-pca" style="font-size: 80%;"> Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#kernel-pca" style="font-size: 80%;"> Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs031.html#other-techniques" style="font-size: 80%;"><b>Other techniques</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs032.html#clustering-and-unsupervised-learning" style="font-size: 80%;"><b>Clustering and Unsupervised Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs033.html#basic-idea-of-the-k-means-clustering-algorithm" style="font-size: 80%;"><b>Basic Idea of the \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs034.html#the-k-means-algorithm" style="font-size: 80%;"><b>The \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs035.html#basic-math-of-the-k-means-algorithm" style="font-size: 80%;"><b>Basic Math of the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs036.html#within-cluster-point-scatter" style="font-size: 80%;"><b>Within Cluster Point Scatter</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs037.html#more-details" style="font-size: 80%;"><b>More Details</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs038.html#total-cluster-variance" style="font-size: 80%;"><b>Total Cluster Variance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs039.html#the-k-means-clustering-algorithm" style="font-size: 80%;"><b>The \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs040.html#summarizing" style="font-size: 80%;"><b>Summarizing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs041.html#writing-our-own-code-the-data-set" style="font-size: 80%;"><b>Writing our own Code, the Data Set</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs042.html#implementing-the-k-means-algorithm" style="font-size: 80%;"><b>Implementing the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs043.html#plotting" style="font-size: 80%;"><b>Plotting</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs044.html#continuing" style="font-size: 80%;"><b>Continuing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs045.html#wrapping-it-up" style="font-size: 80%;"><b>Wrapping it up</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs046.html#summary-of-course" style="font-size: 80%;"><b>Summary of course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs047.html#what-me-worry-no-final-exam-in-this-course" style="font-size: 80%;"><b>What? Me worry? No final exam in this course!</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs048.html#what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks" style="font-size: 80%;"><b>What is the link between Artificial Intelligence and Machine Learning and some general Remarks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs049.html#going-back-to-the-beginning-of-the-semester" style="font-size: 80%;"><b>Going back to the beginning of the semester</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs050.html#not-so-sharp-distinctions" style="font-size: 80%;"><b>Not so sharp distinctions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs051.html#topics-we-have-covered-this-year" style="font-size: 80%;"><b>Topics we have covered this year</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs052.html#statistical-analysis-and-optimization-of-data" style="font-size: 80%;"><b>Statistical analysis and optimization of data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs053.html#machine-learning" style="font-size: 80%;"><b>Machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs054.html#learning-outcomes-and-overarching-aims-of-this-course" style="font-size: 80%;"><b>Learning outcomes and overarching aims of this course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs055.html#perspective-on-machine-learning" style="font-size: 80%;"><b>Perspective on Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs056.html#machine-learning-research" style="font-size: 80%;"><b>Machine Learning Research</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs057.html#starting-your-machine-learning-project" style="font-size: 80%;"><b>Starting your Machine Learning Project</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs058.html#choose-a-model-and-algorithm" style="font-size: 80%;"><b>Choose a Model and Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs059.html#preparing-your-data" style="font-size: 80%;"><b>Preparing Your Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs060.html#which-activation-and-weights-to-choose-in-neural-networks" style="font-size: 80%;"><b>Which Activation and Weights to Choose in Neural Networks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs061.html#optimization-methods-and-hyperparameters" style="font-size: 80%;"><b>Optimization Methods and Hyperparameters</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs062.html#resampling" style="font-size: 80%;"><b>Resampling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs063.html#other-courses-on-data-science-and-machine-learning-at-uio" style="font-size: 80%;"><b>Other courses on Data science and Machine Learning at UiO</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs064.html#additional-courses-of-interest" style="font-size: 80%;"><b>Additional courses of interest</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs065.html#what-s-the-future-like" style="font-size: 80%;"><b>What's the future like?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs066.html#types-of-machine-learning-a-repetition" style="font-size: 80%;"><b>Types of Machine Learning, a repetition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs067.html#why-boltzmann-machines" style="font-size: 80%;"><b>Why Boltzmann machines?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs068.html#boltzmann-machines" style="font-size: 80%;"><b>Boltzmann Machines</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs069.html#some-similarities-and-differences-from-dnns" style="font-size: 80%;"><b>Some similarities and differences from DNNs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs070.html#boltzmann-machines-bm" style="font-size: 80%;"><b>Boltzmann machines (BM)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs071.html#a-standard-bm-setup" style="font-size: 80%;"><b>A standard BM setup</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs072.html#the-structure-of-the-rbm-network" style="font-size: 80%;"><b>The structure of the RBM network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs073.html#the-network" style="font-size: 80%;"><b>The network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs074.html#goals" style="font-size: 80%;"><b>Goals</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs075.html#joint-distribution" style="font-size: 80%;"><b>Joint distribution</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs076.html#network-elements-the-energy-function" style="font-size: 80%;"><b>Network Elements, the energy function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs077.html#defining-different-types-of-rbms" style="font-size: 80%;"><b>Defining different types of RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs078.html#more-about-rbms" style="font-size: 80%;"><b>More about RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs079.html#autoencoders-overarching-view" style="font-size: 80%;"><b>Autoencoders: Overarching view</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs080.html#bayesian-machine-learning" style="font-size: 80%;"><b>Bayesian Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs081.html#reinforcement-learning" style="font-size: 80%;"><b>Reinforcement Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs082.html#transfer-learning" style="font-size: 80%;"><b>Transfer learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs083.html#adversarial-learning" style="font-size: 80%;"><b>Adversarial learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs084.html#dual-learning" style="font-size: 80%;"><b>Dual learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="#distributed-machine-learning" style="font-size: 80%;"><b>Distributed machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs086.html#meta-learning" style="font-size: 80%;"><b>Meta learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs087.html#the-challenges-facing-machine-learning" style="font-size: 80%;"><b>The Challenges Facing Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs088.html#explainable-machine-learning" style="font-size: 80%;"><b>Explainable machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs089.html#scientific-machine-learning" style="font-size: 80%;"><b>Scientific Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs090.html#quantum-machine-learning" style="font-size: 80%;"><b>Quantum machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs091.html#quantum-machine-learning-algorithms-based-on-linear-algebra" style="font-size: 80%;"><b>Quantum machine learning algorithms based on linear algebra</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs092.html#quantum-reinforcement-learning" style="font-size: 80%;"><b>Quantum reinforcement learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs093.html#quantum-deep-learning" style="font-size: 80%;"><b>Quantum deep learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs094.html#social-machine-learning" style="font-size: 80%;"><b>Social machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs095.html#the-last-words" style="font-size: 80%;"><b>The last words?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs096.html#ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean" style="font-size: 80%;"><b>AI/ML and some statements you may have heard (and what do they mean?)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs097.html#best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester" style="font-size: 80%;"><b>Best wishes to you all and thanks so much for your heroic efforts this semester</b></a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div> <!-- end of navigation bar -->
|
||||
<div class="container">
|
||||
<p> </p><p> </p><p> </p> <!-- add vertical space -->
|
||||
<a name="part0085"></a>
|
||||
<!-- !split -->
|
||||
<h2 id="distributed-machine-learning" 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
|
||||
implementing the machine learning algorithms in parallel is required.
|
||||
</p>
|
||||
|
||||
<p>
|
||||
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<li><a href="">...</a></li>
|
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<li><a href="._week47-bs097.html">98</a></li>
|
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|
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|
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|
||||
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|
||||
('Basic ideas of the Principal Component Analysis (PCA)',
|
||||
2,
|
||||
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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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|
||||
('Towards the PCA theorem', 2, None, 'towards-the-pca-theorem'),
|
||||
('More on the PCA Theorem', 2, None, 'more-on-the-pca-theorem'),
|
||||
("A kind of Bird's view on PCA",
|
||||
2,
|
||||
None,
|
||||
'a-kind-of-bird-s-view-on-pca'),
|
||||
('Writing our own PCA code', 2, None, 'writing-our-own-pca-code'),
|
||||
('Implementing it', 2, None, 'implementing-it'),
|
||||
('First Step', 2, None, 'first-step'),
|
||||
('Scaling', 2, None, 'scaling'),
|
||||
('Centered Data', 2, None, 'centered-data'),
|
||||
('Exploring', 2, None, 'exploring'),
|
||||
('Diagonalize the sample covariance matrix to obtain the '
|
||||
'principal components',
|
||||
2,
|
||||
None,
|
||||
'diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components'),
|
||||
('Collecting all Steps', 2, None, 'collecting-all-steps'),
|
||||
('Classical PCA Theorem', 2, None, 'classical-pca-theorem'),
|
||||
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|
||||
('Geometric Interpretation and link with Singular Value '
|
||||
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|
||||
2,
|
||||
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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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|
||||
2,
|
||||
None,
|
||||
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|
||||
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|
||||
2,
|
||||
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|
||||
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|
||||
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|
||||
('Basic Math of the $k$-means Algorithm',
|
||||
2,
|
||||
None,
|
||||
'basic-math-of-the-k-means-algorithm'),
|
||||
('Within Cluster Point Scatter',
|
||||
2,
|
||||
None,
|
||||
'within-cluster-point-scatter'),
|
||||
('More Details', 2, None, 'more-details'),
|
||||
('Total Cluster Variance', 2, None, 'total-cluster-variance'),
|
||||
('The $k$-means Clustering Algorithm',
|
||||
2,
|
||||
None,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
None,
|
||||
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|
||||
('Implementing the $k$-means Algorithm',
|
||||
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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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|
||||
('What is the link between Artificial Intelligence and Machine '
|
||||
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|
||||
2,
|
||||
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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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|
||||
2,
|
||||
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|
||||
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|
||||
('Optimization Methods and Hyperparameters',
|
||||
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|
||||
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|
||||
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|
||||
('Resampling', 2, None, 'resampling'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('Additional courses of interest',
|
||||
2,
|
||||
None,
|
||||
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|
||||
("What's the future like?", 2, None, 'what-s-the-future-like'),
|
||||
('Types of Machine Learning, a repetition',
|
||||
2,
|
||||
None,
|
||||
'types-of-machine-learning-a-repetition'),
|
||||
('Why Boltzmann machines?', 2, None, 'why-boltzmann-machines'),
|
||||
('Boltzmann Machines', 2, None, 'boltzmann-machines'),
|
||||
('Some similarities and differences from DNNs',
|
||||
2,
|
||||
None,
|
||||
'some-similarities-and-differences-from-dnns'),
|
||||
('Boltzmann machines (BM)', 2, None, 'boltzmann-machines-bm'),
|
||||
('A standard BM setup', 2, None, 'a-standard-bm-setup'),
|
||||
('The structure of the RBM network',
|
||||
2,
|
||||
None,
|
||||
'the-structure-of-the-rbm-network'),
|
||||
('The network', 2, None, 'the-network'),
|
||||
('Goals', 2, None, 'goals'),
|
||||
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|
||||
('Network Elements, the energy function',
|
||||
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|
||||
None,
|
||||
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|
||||
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|
||||
2,
|
||||
None,
|
||||
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|
||||
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|
||||
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|
||||
2,
|
||||
None,
|
||||
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|
||||
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|
||||
2,
|
||||
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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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|
||||
2,
|
||||
None,
|
||||
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|
||||
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|
||||
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|
||||
2,
|
||||
None,
|
||||
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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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|
||||
2,
|
||||
None,
|
||||
'quantum-machine-learning-algorithms-based-on-linear-algebra'),
|
||||
('Quantum reinforcement learning',
|
||||
2,
|
||||
None,
|
||||
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|
||||
('Quantum deep learning', 2, None, 'quantum-deep-learning'),
|
||||
('Social machine learning', 2, None, 'social-machine-learning'),
|
||||
('The last words?', 2, None, 'the-last-words'),
|
||||
('AI/ML and some statements you may have heard (and what do they '
|
||||
'mean?)',
|
||||
2,
|
||||
None,
|
||||
'ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester')]}
|
||||
end of tocinfo -->
|
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<a class="navbar-brand" href="week47-bs.html">Week 47: Unsupervised learning (PCA and Clustering) 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="._week47-bs001.html#overview-of-week-47" style="font-size: 80%;"><b>Overview of week 47</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs002.html#basic-ideas-of-the-principal-component-analysis-pca" style="font-size: 80%;"><b>Basic ideas of the Principal Component Analysis (PCA)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs003.html#introducing-the-covariance-and-correlation-functions" style="font-size: 80%;"><b>Introducing the Covariance and Correlation functions</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs004.html#more-on-the-covariance" style="font-size: 80%;"><b>More on the covariance</b></a></li>
|
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<!-- navigation toc: --> <li><a href="._week47-bs005.html#reminding-ourselves-about-linear-regression" style="font-size: 80%;"><b>Reminding ourselves about Linear Regression</b></a></li>
|
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<!-- navigation toc: --> <li><a href="._week47-bs006.html#simple-example" style="font-size: 80%;"><b>Simple Example</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs007.html#the-correlation-matrix" style="font-size: 80%;"><b>The Correlation Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs008.html#numpy-functionality" style="font-size: 80%;"><b>Numpy Functionality</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs009.html#correlation-matrix-again" style="font-size: 80%;"><b>Correlation Matrix again</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs010.html#using-pandas" style="font-size: 80%;"><b>Using Pandas</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs011.html#and-then-the-franke-function" style="font-size: 80%;"><b>And then the Franke Function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs012.html#links-with-the-design-matrix" style="font-size: 80%;"><b>Links with the Design Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs013.html#computing-the-expectation-values" style="font-size: 80%;"><b>Computing the Expectation Values</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs014.html#towards-the-pca-theorem" style="font-size: 80%;"><b>Towards the PCA theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs015.html#more-on-the-pca-theorem" style="font-size: 80%;"><b>More on the PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs016.html#a-kind-of-bird-s-view-on-pca" style="font-size: 80%;"><b>A kind of Bird's view on PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs017.html#writing-our-own-pca-code" style="font-size: 80%;"><b>Writing our own PCA code</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs018.html#implementing-it" style="font-size: 80%;"><b>Implementing it</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs019.html#first-step" style="font-size: 80%;"><b>First Step</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs020.html#scaling" style="font-size: 80%;"><b>Scaling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs021.html#centered-data" style="font-size: 80%;"><b>Centered Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs022.html#exploring" style="font-size: 80%;"><b>Exploring</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs023.html#diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components" style="font-size: 80%;"><b>Diagonalize the sample covariance matrix to obtain the principal components</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs024.html#collecting-all-steps" style="font-size: 80%;"><b>Collecting all Steps</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs025.html#classical-pca-theorem" style="font-size: 80%;"><b>Classical PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs026.html#the-pca-theorem" style="font-size: 80%;"><b>The PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs027.html#geometric-interpretation-and-link-with-singular-value-decomposition" style="font-size: 80%;"><b>Geometric Interpretation and link with Singular Value Decomposition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs028.html#pca-and-scikit-learn" style="font-size: 80%;"><b>PCA and scikit-learn</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs029.html#back-to-the-cancer-data" style="font-size: 80%;"><b>Back to the Cancer Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#incremental-pca" style="font-size: 80%;"><b>Incremental PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#randomized-pca" style="font-size: 80%;"> Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#kernel-pca" style="font-size: 80%;"> Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs031.html#other-techniques" style="font-size: 80%;"><b>Other techniques</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs032.html#clustering-and-unsupervised-learning" style="font-size: 80%;"><b>Clustering and Unsupervised Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs033.html#basic-idea-of-the-k-means-clustering-algorithm" style="font-size: 80%;"><b>Basic Idea of the \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs034.html#the-k-means-algorithm" style="font-size: 80%;"><b>The \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs035.html#basic-math-of-the-k-means-algorithm" style="font-size: 80%;"><b>Basic Math of the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs036.html#within-cluster-point-scatter" style="font-size: 80%;"><b>Within Cluster Point Scatter</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs037.html#more-details" style="font-size: 80%;"><b>More Details</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs038.html#total-cluster-variance" style="font-size: 80%;"><b>Total Cluster Variance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs039.html#the-k-means-clustering-algorithm" style="font-size: 80%;"><b>The \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs040.html#summarizing" style="font-size: 80%;"><b>Summarizing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs041.html#writing-our-own-code-the-data-set" style="font-size: 80%;"><b>Writing our own Code, the Data Set</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs042.html#implementing-the-k-means-algorithm" style="font-size: 80%;"><b>Implementing the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs043.html#plotting" style="font-size: 80%;"><b>Plotting</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs044.html#continuing" style="font-size: 80%;"><b>Continuing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs045.html#wrapping-it-up" style="font-size: 80%;"><b>Wrapping it up</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs046.html#summary-of-course" style="font-size: 80%;"><b>Summary of course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs047.html#what-me-worry-no-final-exam-in-this-course" style="font-size: 80%;"><b>What? Me worry? No final exam in this course!</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs048.html#what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks" style="font-size: 80%;"><b>What is the link between Artificial Intelligence and Machine Learning and some general Remarks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs049.html#going-back-to-the-beginning-of-the-semester" style="font-size: 80%;"><b>Going back to the beginning of the semester</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs050.html#not-so-sharp-distinctions" style="font-size: 80%;"><b>Not so sharp distinctions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs051.html#topics-we-have-covered-this-year" style="font-size: 80%;"><b>Topics we have covered this year</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs052.html#statistical-analysis-and-optimization-of-data" style="font-size: 80%;"><b>Statistical analysis and optimization of data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs053.html#machine-learning" style="font-size: 80%;"><b>Machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs054.html#learning-outcomes-and-overarching-aims-of-this-course" style="font-size: 80%;"><b>Learning outcomes and overarching aims of this course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs055.html#perspective-on-machine-learning" style="font-size: 80%;"><b>Perspective on Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs056.html#machine-learning-research" style="font-size: 80%;"><b>Machine Learning Research</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs057.html#starting-your-machine-learning-project" style="font-size: 80%;"><b>Starting your Machine Learning Project</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs058.html#choose-a-model-and-algorithm" style="font-size: 80%;"><b>Choose a Model and Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs059.html#preparing-your-data" style="font-size: 80%;"><b>Preparing Your Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs060.html#which-activation-and-weights-to-choose-in-neural-networks" style="font-size: 80%;"><b>Which Activation and Weights to Choose in Neural Networks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs061.html#optimization-methods-and-hyperparameters" style="font-size: 80%;"><b>Optimization Methods and Hyperparameters</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs062.html#resampling" style="font-size: 80%;"><b>Resampling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs063.html#other-courses-on-data-science-and-machine-learning-at-uio" style="font-size: 80%;"><b>Other courses on Data science and Machine Learning at UiO</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs064.html#additional-courses-of-interest" style="font-size: 80%;"><b>Additional courses of interest</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs065.html#what-s-the-future-like" style="font-size: 80%;"><b>What's the future like?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs066.html#types-of-machine-learning-a-repetition" style="font-size: 80%;"><b>Types of Machine Learning, a repetition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs067.html#why-boltzmann-machines" style="font-size: 80%;"><b>Why Boltzmann machines?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs068.html#boltzmann-machines" style="font-size: 80%;"><b>Boltzmann Machines</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs069.html#some-similarities-and-differences-from-dnns" style="font-size: 80%;"><b>Some similarities and differences from DNNs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs070.html#boltzmann-machines-bm" style="font-size: 80%;"><b>Boltzmann machines (BM)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs071.html#a-standard-bm-setup" style="font-size: 80%;"><b>A standard BM setup</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs072.html#the-structure-of-the-rbm-network" style="font-size: 80%;"><b>The structure of the RBM network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs073.html#the-network" style="font-size: 80%;"><b>The network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs074.html#goals" style="font-size: 80%;"><b>Goals</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs075.html#joint-distribution" style="font-size: 80%;"><b>Joint distribution</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs076.html#network-elements-the-energy-function" style="font-size: 80%;"><b>Network Elements, the energy function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs077.html#defining-different-types-of-rbms" style="font-size: 80%;"><b>Defining different types of RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs078.html#more-about-rbms" style="font-size: 80%;"><b>More about RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs079.html#autoencoders-overarching-view" style="font-size: 80%;"><b>Autoencoders: Overarching view</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs080.html#bayesian-machine-learning" style="font-size: 80%;"><b>Bayesian Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs081.html#reinforcement-learning" style="font-size: 80%;"><b>Reinforcement Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs082.html#transfer-learning" style="font-size: 80%;"><b>Transfer learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs083.html#adversarial-learning" style="font-size: 80%;"><b>Adversarial learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs084.html#dual-learning" style="font-size: 80%;"><b>Dual learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs085.html#distributed-machine-learning" style="font-size: 80%;"><b>Distributed machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="#meta-learning" style="font-size: 80%;"><b>Meta learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs087.html#the-challenges-facing-machine-learning" style="font-size: 80%;"><b>The Challenges Facing Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs088.html#explainable-machine-learning" style="font-size: 80%;"><b>Explainable machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs089.html#scientific-machine-learning" style="font-size: 80%;"><b>Scientific Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs090.html#quantum-machine-learning" style="font-size: 80%;"><b>Quantum machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs091.html#quantum-machine-learning-algorithms-based-on-linear-algebra" style="font-size: 80%;"><b>Quantum machine learning algorithms based on linear algebra</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs092.html#quantum-reinforcement-learning" style="font-size: 80%;"><b>Quantum reinforcement learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs093.html#quantum-deep-learning" style="font-size: 80%;"><b>Quantum deep learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs094.html#social-machine-learning" style="font-size: 80%;"><b>Social machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs095.html#the-last-words" style="font-size: 80%;"><b>The last words?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs096.html#ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean" style="font-size: 80%;"><b>AI/ML and some statements you may have heard (and what do they mean?)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs097.html#best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester" style="font-size: 80%;"><b>Best wishes to you all and thanks so much for your heroic efforts this semester</b></a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
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|
||||
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|
||||
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|
||||
<p> </p><p> </p><p> </p> <!-- add vertical space -->
|
||||
<a name="part0086"></a>
|
||||
<!-- !split -->
|
||||
<h2 id="meta-learning" 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
|
||||
tasks.
|
||||
</p>
|
||||
|
||||
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<li><a href="._week47-bs097.html">98</a></li>
|
||||
<li><a href="._week47-bs087.html">»</a></li>
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|
||||
('Basic ideas of the Principal Component Analysis (PCA)',
|
||||
2,
|
||||
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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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|
||||
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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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|
||||
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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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|
||||
2,
|
||||
None,
|
||||
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|
||||
('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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<a class="navbar-brand" href="week47-bs.html">Week 47: Unsupervised learning (PCA and Clustering) and Summary of Course</a>
|
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|
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|
||||
<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
|
||||
<ul class="dropdown-menu">
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs001.html#overview-of-week-47" style="font-size: 80%;"><b>Overview of week 47</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs002.html#basic-ideas-of-the-principal-component-analysis-pca" style="font-size: 80%;"><b>Basic ideas of the Principal Component Analysis (PCA)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs003.html#introducing-the-covariance-and-correlation-functions" style="font-size: 80%;"><b>Introducing the Covariance and Correlation functions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs004.html#more-on-the-covariance" style="font-size: 80%;"><b>More on the covariance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs005.html#reminding-ourselves-about-linear-regression" style="font-size: 80%;"><b>Reminding ourselves about Linear Regression</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs006.html#simple-example" style="font-size: 80%;"><b>Simple Example</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs007.html#the-correlation-matrix" style="font-size: 80%;"><b>The Correlation Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs008.html#numpy-functionality" style="font-size: 80%;"><b>Numpy Functionality</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs009.html#correlation-matrix-again" style="font-size: 80%;"><b>Correlation Matrix again</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs010.html#using-pandas" style="font-size: 80%;"><b>Using Pandas</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs011.html#and-then-the-franke-function" style="font-size: 80%;"><b>And then the Franke Function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs012.html#links-with-the-design-matrix" style="font-size: 80%;"><b>Links with the Design Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs013.html#computing-the-expectation-values" style="font-size: 80%;"><b>Computing the Expectation Values</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs014.html#towards-the-pca-theorem" style="font-size: 80%;"><b>Towards the PCA theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs015.html#more-on-the-pca-theorem" style="font-size: 80%;"><b>More on the PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs016.html#a-kind-of-bird-s-view-on-pca" style="font-size: 80%;"><b>A kind of Bird's view on PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs017.html#writing-our-own-pca-code" style="font-size: 80%;"><b>Writing our own PCA code</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs018.html#implementing-it" style="font-size: 80%;"><b>Implementing it</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs019.html#first-step" style="font-size: 80%;"><b>First Step</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs020.html#scaling" style="font-size: 80%;"><b>Scaling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs021.html#centered-data" style="font-size: 80%;"><b>Centered Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs022.html#exploring" style="font-size: 80%;"><b>Exploring</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs023.html#diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components" style="font-size: 80%;"><b>Diagonalize the sample covariance matrix to obtain the principal components</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs024.html#collecting-all-steps" style="font-size: 80%;"><b>Collecting all Steps</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs025.html#classical-pca-theorem" style="font-size: 80%;"><b>Classical PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs026.html#the-pca-theorem" style="font-size: 80%;"><b>The PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs027.html#geometric-interpretation-and-link-with-singular-value-decomposition" style="font-size: 80%;"><b>Geometric Interpretation and link with Singular Value Decomposition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs028.html#pca-and-scikit-learn" style="font-size: 80%;"><b>PCA and scikit-learn</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs029.html#back-to-the-cancer-data" style="font-size: 80%;"><b>Back to the Cancer Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#incremental-pca" style="font-size: 80%;"><b>Incremental PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#randomized-pca" style="font-size: 80%;"> Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#kernel-pca" style="font-size: 80%;"> Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs031.html#other-techniques" style="font-size: 80%;"><b>Other techniques</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs032.html#clustering-and-unsupervised-learning" style="font-size: 80%;"><b>Clustering and Unsupervised Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs033.html#basic-idea-of-the-k-means-clustering-algorithm" style="font-size: 80%;"><b>Basic Idea of the \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs034.html#the-k-means-algorithm" style="font-size: 80%;"><b>The \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs035.html#basic-math-of-the-k-means-algorithm" style="font-size: 80%;"><b>Basic Math of the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs036.html#within-cluster-point-scatter" style="font-size: 80%;"><b>Within Cluster Point Scatter</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs037.html#more-details" style="font-size: 80%;"><b>More Details</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs038.html#total-cluster-variance" style="font-size: 80%;"><b>Total Cluster Variance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs039.html#the-k-means-clustering-algorithm" style="font-size: 80%;"><b>The \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs040.html#summarizing" style="font-size: 80%;"><b>Summarizing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs041.html#writing-our-own-code-the-data-set" style="font-size: 80%;"><b>Writing our own Code, the Data Set</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs042.html#implementing-the-k-means-algorithm" style="font-size: 80%;"><b>Implementing the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs043.html#plotting" style="font-size: 80%;"><b>Plotting</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs044.html#continuing" style="font-size: 80%;"><b>Continuing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs045.html#wrapping-it-up" style="font-size: 80%;"><b>Wrapping it up</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs046.html#summary-of-course" style="font-size: 80%;"><b>Summary of course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs047.html#what-me-worry-no-final-exam-in-this-course" style="font-size: 80%;"><b>What? Me worry? No final exam in this course!</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs048.html#what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks" style="font-size: 80%;"><b>What is the link between Artificial Intelligence and Machine Learning and some general Remarks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs049.html#going-back-to-the-beginning-of-the-semester" style="font-size: 80%;"><b>Going back to the beginning of the semester</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs050.html#not-so-sharp-distinctions" style="font-size: 80%;"><b>Not so sharp distinctions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs051.html#topics-we-have-covered-this-year" style="font-size: 80%;"><b>Topics we have covered this year</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs052.html#statistical-analysis-and-optimization-of-data" style="font-size: 80%;"><b>Statistical analysis and optimization of data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs053.html#machine-learning" style="font-size: 80%;"><b>Machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs054.html#learning-outcomes-and-overarching-aims-of-this-course" style="font-size: 80%;"><b>Learning outcomes and overarching aims of this course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs055.html#perspective-on-machine-learning" style="font-size: 80%;"><b>Perspective on Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs056.html#machine-learning-research" style="font-size: 80%;"><b>Machine Learning Research</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs057.html#starting-your-machine-learning-project" style="font-size: 80%;"><b>Starting your Machine Learning Project</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs058.html#choose-a-model-and-algorithm" style="font-size: 80%;"><b>Choose a Model and Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs059.html#preparing-your-data" style="font-size: 80%;"><b>Preparing Your Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs060.html#which-activation-and-weights-to-choose-in-neural-networks" style="font-size: 80%;"><b>Which Activation and Weights to Choose in Neural Networks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs061.html#optimization-methods-and-hyperparameters" style="font-size: 80%;"><b>Optimization Methods and Hyperparameters</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs062.html#resampling" style="font-size: 80%;"><b>Resampling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs063.html#other-courses-on-data-science-and-machine-learning-at-uio" style="font-size: 80%;"><b>Other courses on Data science and Machine Learning at UiO</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs064.html#additional-courses-of-interest" style="font-size: 80%;"><b>Additional courses of interest</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs065.html#what-s-the-future-like" style="font-size: 80%;"><b>What's the future like?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs066.html#types-of-machine-learning-a-repetition" style="font-size: 80%;"><b>Types of Machine Learning, a repetition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs067.html#why-boltzmann-machines" style="font-size: 80%;"><b>Why Boltzmann machines?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs068.html#boltzmann-machines" style="font-size: 80%;"><b>Boltzmann Machines</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs069.html#some-similarities-and-differences-from-dnns" style="font-size: 80%;"><b>Some similarities and differences from DNNs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs070.html#boltzmann-machines-bm" style="font-size: 80%;"><b>Boltzmann machines (BM)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs071.html#a-standard-bm-setup" style="font-size: 80%;"><b>A standard BM setup</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs072.html#the-structure-of-the-rbm-network" style="font-size: 80%;"><b>The structure of the RBM network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs073.html#the-network" style="font-size: 80%;"><b>The network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs074.html#goals" style="font-size: 80%;"><b>Goals</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs075.html#joint-distribution" style="font-size: 80%;"><b>Joint distribution</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs076.html#network-elements-the-energy-function" style="font-size: 80%;"><b>Network Elements, the energy function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs077.html#defining-different-types-of-rbms" style="font-size: 80%;"><b>Defining different types of RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs078.html#more-about-rbms" style="font-size: 80%;"><b>More about RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs079.html#autoencoders-overarching-view" style="font-size: 80%;"><b>Autoencoders: Overarching view</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs080.html#bayesian-machine-learning" style="font-size: 80%;"><b>Bayesian Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs081.html#reinforcement-learning" style="font-size: 80%;"><b>Reinforcement Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs082.html#transfer-learning" style="font-size: 80%;"><b>Transfer learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs083.html#adversarial-learning" style="font-size: 80%;"><b>Adversarial learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs084.html#dual-learning" style="font-size: 80%;"><b>Dual learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs085.html#distributed-machine-learning" style="font-size: 80%;"><b>Distributed machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs086.html#meta-learning" style="font-size: 80%;"><b>Meta learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="#the-challenges-facing-machine-learning" style="font-size: 80%;"><b>The Challenges Facing Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs088.html#explainable-machine-learning" style="font-size: 80%;"><b>Explainable machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs089.html#scientific-machine-learning" style="font-size: 80%;"><b>Scientific Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs090.html#quantum-machine-learning" style="font-size: 80%;"><b>Quantum machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs091.html#quantum-machine-learning-algorithms-based-on-linear-algebra" style="font-size: 80%;"><b>Quantum machine learning algorithms based on linear algebra</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs092.html#quantum-reinforcement-learning" style="font-size: 80%;"><b>Quantum reinforcement learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs093.html#quantum-deep-learning" style="font-size: 80%;"><b>Quantum deep learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs094.html#social-machine-learning" style="font-size: 80%;"><b>Social machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs095.html#the-last-words" style="font-size: 80%;"><b>The last words?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs096.html#ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean" style="font-size: 80%;"><b>AI/ML and some statements you may have heard (and what do they mean?)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs097.html#best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester" style="font-size: 80%;"><b>Best wishes to you all and thanks so much for your heroic efforts this semester</b></a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div> <!-- end of navigation bar -->
|
||||
<div class="container">
|
||||
<p> </p><p> </p><p> </p> <!-- add vertical space -->
|
||||
<a name="part0087"></a>
|
||||
<!-- !split -->
|
||||
<h2 id="the-challenges-facing-machine-learning" class="anchor">The Challenges Facing Machine Learning </h2>
|
||||
|
||||
<p>While there has been much progress in machine learning, there are also challenges.</p>
|
||||
|
||||
<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>See the article on <a href="https://www.frontiersin.org/articles/10.3389/frai.2020.00025/full" target="_self">Discovery of Physics From Data: Universal Laws and Discrepancies</a></p>
|
||||
|
||||
<p>
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||||
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<li><a href="">...</a></li>
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<!-- tocinfo
|
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{'highest level': 2,
|
||||
'sections': [('Overview of week 47', 2, None, 'overview-of-week-47'),
|
||||
('Basic ideas of the Principal Component Analysis (PCA)',
|
||||
2,
|
||||
None,
|
||||
'basic-ideas-of-the-principal-component-analysis-pca'),
|
||||
('Introducing the Covariance and Correlation functions',
|
||||
2,
|
||||
None,
|
||||
'introducing-the-covariance-and-correlation-functions'),
|
||||
('More on the covariance', 2, None, 'more-on-the-covariance'),
|
||||
('Reminding ourselves about Linear Regression',
|
||||
2,
|
||||
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|
||||
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|
||||
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|
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|
||||
('Numpy Functionality', 2, None, 'numpy-functionality'),
|
||||
('Correlation Matrix again', 2, None, 'correlation-matrix-again'),
|
||||
('Using Pandas', 2, None, 'using-pandas'),
|
||||
('And then the Franke Function',
|
||||
2,
|
||||
None,
|
||||
'and-then-the-franke-function'),
|
||||
('Links with the Design Matrix',
|
||||
2,
|
||||
None,
|
||||
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|
||||
('Computing the Expectation Values',
|
||||
2,
|
||||
None,
|
||||
'computing-the-expectation-values'),
|
||||
('Towards the PCA theorem', 2, None, 'towards-the-pca-theorem'),
|
||||
('More on the PCA Theorem', 2, None, 'more-on-the-pca-theorem'),
|
||||
("A kind of Bird's view on PCA",
|
||||
2,
|
||||
None,
|
||||
'a-kind-of-bird-s-view-on-pca'),
|
||||
('Writing our own PCA code', 2, None, 'writing-our-own-pca-code'),
|
||||
('Implementing it', 2, None, 'implementing-it'),
|
||||
('First Step', 2, None, 'first-step'),
|
||||
('Scaling', 2, None, 'scaling'),
|
||||
('Centered Data', 2, None, 'centered-data'),
|
||||
('Exploring', 2, None, 'exploring'),
|
||||
('Diagonalize the sample covariance matrix to obtain the '
|
||||
'principal components',
|
||||
2,
|
||||
None,
|
||||
'diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components'),
|
||||
('Collecting all Steps', 2, None, 'collecting-all-steps'),
|
||||
('Classical PCA Theorem', 2, None, 'classical-pca-theorem'),
|
||||
('The PCA Theorem', 2, None, 'the-pca-theorem'),
|
||||
('Geometric Interpretation and link with Singular Value '
|
||||
'Decomposition',
|
||||
2,
|
||||
None,
|
||||
'geometric-interpretation-and-link-with-singular-value-decomposition'),
|
||||
('PCA and scikit-learn', 2, None, 'pca-and-scikit-learn'),
|
||||
('Back to the Cancer Data', 2, None, 'back-to-the-cancer-data'),
|
||||
('Incremental PCA', 2, None, 'incremental-pca'),
|
||||
('Randomized PCA', 3, None, 'randomized-pca'),
|
||||
('Kernel PCA', 3, None, 'kernel-pca'),
|
||||
('Other techniques', 2, None, 'other-techniques'),
|
||||
('Clustering and Unsupervised Learning',
|
||||
2,
|
||||
None,
|
||||
'clustering-and-unsupervised-learning'),
|
||||
('Basic Idea of the $k$-means Clustering Algorithm',
|
||||
2,
|
||||
None,
|
||||
'basic-idea-of-the-k-means-clustering-algorithm'),
|
||||
('The $k$-means Algorithm', 2, None, 'the-k-means-algorithm'),
|
||||
('Basic Math of the $k$-means Algorithm',
|
||||
2,
|
||||
None,
|
||||
'basic-math-of-the-k-means-algorithm'),
|
||||
('Within Cluster Point Scatter',
|
||||
2,
|
||||
None,
|
||||
'within-cluster-point-scatter'),
|
||||
('More Details', 2, None, 'more-details'),
|
||||
('Total Cluster Variance', 2, None, 'total-cluster-variance'),
|
||||
('The $k$-means Clustering Algorithm',
|
||||
2,
|
||||
None,
|
||||
'the-k-means-clustering-algorithm'),
|
||||
('Summarizing', 2, None, 'summarizing'),
|
||||
('Writing our own Code, the Data Set',
|
||||
2,
|
||||
None,
|
||||
'writing-our-own-code-the-data-set'),
|
||||
('Implementing the $k$-means Algorithm',
|
||||
2,
|
||||
None,
|
||||
'implementing-the-k-means-algorithm'),
|
||||
('Plotting', 2, None, 'plotting'),
|
||||
('Continuing', 2, None, 'continuing'),
|
||||
('Wrapping it up', 2, None, 'wrapping-it-up'),
|
||||
('Summary of course', 2, None, 'summary-of-course'),
|
||||
('What? Me worry? No final exam in this course!',
|
||||
2,
|
||||
None,
|
||||
'what-me-worry-no-final-exam-in-this-course'),
|
||||
('What is the link between Artificial Intelligence and Machine '
|
||||
'Learning and some general Remarks',
|
||||
2,
|
||||
None,
|
||||
'what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks'),
|
||||
('Going back to the beginning of the semester',
|
||||
2,
|
||||
None,
|
||||
'going-back-to-the-beginning-of-the-semester'),
|
||||
('Not so sharp distinctions',
|
||||
2,
|
||||
None,
|
||||
'not-so-sharp-distinctions'),
|
||||
('Topics we have covered this year',
|
||||
2,
|
||||
None,
|
||||
'topics-we-have-covered-this-year'),
|
||||
('Statistical analysis and optimization of data',
|
||||
2,
|
||||
None,
|
||||
'statistical-analysis-and-optimization-of-data'),
|
||||
('Machine learning', 2, None, 'machine-learning'),
|
||||
('Learning outcomes and overarching aims of this course',
|
||||
2,
|
||||
None,
|
||||
'learning-outcomes-and-overarching-aims-of-this-course'),
|
||||
('Perspective on Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'perspective-on-machine-learning'),
|
||||
('Machine Learning Research',
|
||||
2,
|
||||
None,
|
||||
'machine-learning-research'),
|
||||
('Starting your Machine Learning Project',
|
||||
2,
|
||||
None,
|
||||
'starting-your-machine-learning-project'),
|
||||
('Choose a Model and Algorithm',
|
||||
2,
|
||||
None,
|
||||
'choose-a-model-and-algorithm'),
|
||||
('Preparing Your Data', 2, None, 'preparing-your-data'),
|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
||||
2,
|
||||
None,
|
||||
'which-activation-and-weights-to-choose-in-neural-networks'),
|
||||
('Optimization Methods and Hyperparameters',
|
||||
2,
|
||||
None,
|
||||
'optimization-methods-and-hyperparameters'),
|
||||
('Resampling', 2, None, 'resampling'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
||||
'other-courses-on-data-science-and-machine-learning-at-uio'),
|
||||
('Additional courses of interest',
|
||||
2,
|
||||
None,
|
||||
'additional-courses-of-interest'),
|
||||
("What's the future like?", 2, None, 'what-s-the-future-like'),
|
||||
('Types of Machine Learning, a repetition',
|
||||
2,
|
||||
None,
|
||||
'types-of-machine-learning-a-repetition'),
|
||||
('Why Boltzmann machines?', 2, None, 'why-boltzmann-machines'),
|
||||
('Boltzmann Machines', 2, None, 'boltzmann-machines'),
|
||||
('Some similarities and differences from DNNs',
|
||||
2,
|
||||
None,
|
||||
'some-similarities-and-differences-from-dnns'),
|
||||
('Boltzmann machines (BM)', 2, None, 'boltzmann-machines-bm'),
|
||||
('A standard BM setup', 2, None, 'a-standard-bm-setup'),
|
||||
('The structure of the RBM network',
|
||||
2,
|
||||
None,
|
||||
'the-structure-of-the-rbm-network'),
|
||||
('The network', 2, None, 'the-network'),
|
||||
('Goals', 2, None, 'goals'),
|
||||
('Joint distribution', 2, None, 'joint-distribution'),
|
||||
('Network Elements, the energy function',
|
||||
2,
|
||||
None,
|
||||
'network-elements-the-energy-function'),
|
||||
('Defining different types of RBMs',
|
||||
2,
|
||||
None,
|
||||
'defining-different-types-of-rbms'),
|
||||
('More about RBMs', 2, None, 'more-about-rbms'),
|
||||
('Autoencoders: Overarching view',
|
||||
2,
|
||||
None,
|
||||
'autoencoders-overarching-view'),
|
||||
('Bayesian Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'bayesian-machine-learning'),
|
||||
('Reinforcement Learning', 2, None, 'reinforcement-learning'),
|
||||
('Transfer learning', 2, None, 'transfer-learning'),
|
||||
('Adversarial learning', 2, None, 'adversarial-learning'),
|
||||
('Dual learning', 2, None, 'dual-learning'),
|
||||
('Distributed machine learning',
|
||||
2,
|
||||
None,
|
||||
'distributed-machine-learning'),
|
||||
('Meta learning', 2, None, 'meta-learning'),
|
||||
('The Challenges Facing Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'the-challenges-facing-machine-learning'),
|
||||
('Explainable machine learning',
|
||||
2,
|
||||
None,
|
||||
'explainable-machine-learning'),
|
||||
('Scientific Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'scientific-machine-learning'),
|
||||
('Quantum machine learning', 2, None, 'quantum-machine-learning'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'quantum-machine-learning-algorithms-based-on-linear-algebra'),
|
||||
('Quantum reinforcement learning',
|
||||
2,
|
||||
None,
|
||||
'quantum-reinforcement-learning'),
|
||||
('Quantum deep learning', 2, None, 'quantum-deep-learning'),
|
||||
('Social machine learning', 2, None, 'social-machine-learning'),
|
||||
('The last words?', 2, None, 'the-last-words'),
|
||||
('AI/ML and some statements you may have heard (and what do they '
|
||||
'mean?)',
|
||||
2,
|
||||
None,
|
||||
'ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester')]}
|
||||
end of tocinfo -->
|
||||
|
||||
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|
||||
|
||||
|
||||
|
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|
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<a class="navbar-brand" href="week47-bs.html">Week 47: Unsupervised learning (PCA and Clustering) and Summary of Course</a>
|
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|
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|
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<ul class="nav navbar-nav navbar-right">
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<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
|
||||
<ul class="dropdown-menu">
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs001.html#overview-of-week-47" style="font-size: 80%;"><b>Overview of week 47</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs002.html#basic-ideas-of-the-principal-component-analysis-pca" style="font-size: 80%;"><b>Basic ideas of the Principal Component Analysis (PCA)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs003.html#introducing-the-covariance-and-correlation-functions" style="font-size: 80%;"><b>Introducing the Covariance and Correlation functions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs004.html#more-on-the-covariance" style="font-size: 80%;"><b>More on the covariance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs005.html#reminding-ourselves-about-linear-regression" style="font-size: 80%;"><b>Reminding ourselves about Linear Regression</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs006.html#simple-example" style="font-size: 80%;"><b>Simple Example</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs007.html#the-correlation-matrix" style="font-size: 80%;"><b>The Correlation Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs008.html#numpy-functionality" style="font-size: 80%;"><b>Numpy Functionality</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs009.html#correlation-matrix-again" style="font-size: 80%;"><b>Correlation Matrix again</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs010.html#using-pandas" style="font-size: 80%;"><b>Using Pandas</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs011.html#and-then-the-franke-function" style="font-size: 80%;"><b>And then the Franke Function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs012.html#links-with-the-design-matrix" style="font-size: 80%;"><b>Links with the Design Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs013.html#computing-the-expectation-values" style="font-size: 80%;"><b>Computing the Expectation Values</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs014.html#towards-the-pca-theorem" style="font-size: 80%;"><b>Towards the PCA theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs015.html#more-on-the-pca-theorem" style="font-size: 80%;"><b>More on the PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs016.html#a-kind-of-bird-s-view-on-pca" style="font-size: 80%;"><b>A kind of Bird's view on PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs017.html#writing-our-own-pca-code" style="font-size: 80%;"><b>Writing our own PCA code</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs018.html#implementing-it" style="font-size: 80%;"><b>Implementing it</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs019.html#first-step" style="font-size: 80%;"><b>First Step</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs020.html#scaling" style="font-size: 80%;"><b>Scaling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs021.html#centered-data" style="font-size: 80%;"><b>Centered Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs022.html#exploring" style="font-size: 80%;"><b>Exploring</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs023.html#diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components" style="font-size: 80%;"><b>Diagonalize the sample covariance matrix to obtain the principal components</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs024.html#collecting-all-steps" style="font-size: 80%;"><b>Collecting all Steps</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs025.html#classical-pca-theorem" style="font-size: 80%;"><b>Classical PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs026.html#the-pca-theorem" style="font-size: 80%;"><b>The PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs027.html#geometric-interpretation-and-link-with-singular-value-decomposition" style="font-size: 80%;"><b>Geometric Interpretation and link with Singular Value Decomposition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs028.html#pca-and-scikit-learn" style="font-size: 80%;"><b>PCA and scikit-learn</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs029.html#back-to-the-cancer-data" style="font-size: 80%;"><b>Back to the Cancer Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#incremental-pca" style="font-size: 80%;"><b>Incremental PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#randomized-pca" style="font-size: 80%;"> Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#kernel-pca" style="font-size: 80%;"> Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs031.html#other-techniques" style="font-size: 80%;"><b>Other techniques</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs032.html#clustering-and-unsupervised-learning" style="font-size: 80%;"><b>Clustering and Unsupervised Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs033.html#basic-idea-of-the-k-means-clustering-algorithm" style="font-size: 80%;"><b>Basic Idea of the \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs034.html#the-k-means-algorithm" style="font-size: 80%;"><b>The \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs035.html#basic-math-of-the-k-means-algorithm" style="font-size: 80%;"><b>Basic Math of the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs036.html#within-cluster-point-scatter" style="font-size: 80%;"><b>Within Cluster Point Scatter</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs037.html#more-details" style="font-size: 80%;"><b>More Details</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs038.html#total-cluster-variance" style="font-size: 80%;"><b>Total Cluster Variance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs039.html#the-k-means-clustering-algorithm" style="font-size: 80%;"><b>The \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs040.html#summarizing" style="font-size: 80%;"><b>Summarizing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs041.html#writing-our-own-code-the-data-set" style="font-size: 80%;"><b>Writing our own Code, the Data Set</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs042.html#implementing-the-k-means-algorithm" style="font-size: 80%;"><b>Implementing the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs043.html#plotting" style="font-size: 80%;"><b>Plotting</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs044.html#continuing" style="font-size: 80%;"><b>Continuing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs045.html#wrapping-it-up" style="font-size: 80%;"><b>Wrapping it up</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs046.html#summary-of-course" style="font-size: 80%;"><b>Summary of course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs047.html#what-me-worry-no-final-exam-in-this-course" style="font-size: 80%;"><b>What? Me worry? No final exam in this course!</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs048.html#what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks" style="font-size: 80%;"><b>What is the link between Artificial Intelligence and Machine Learning and some general Remarks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs049.html#going-back-to-the-beginning-of-the-semester" style="font-size: 80%;"><b>Going back to the beginning of the semester</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs050.html#not-so-sharp-distinctions" style="font-size: 80%;"><b>Not so sharp distinctions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs051.html#topics-we-have-covered-this-year" style="font-size: 80%;"><b>Topics we have covered this year</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs052.html#statistical-analysis-and-optimization-of-data" style="font-size: 80%;"><b>Statistical analysis and optimization of data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs053.html#machine-learning" style="font-size: 80%;"><b>Machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs054.html#learning-outcomes-and-overarching-aims-of-this-course" style="font-size: 80%;"><b>Learning outcomes and overarching aims of this course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs055.html#perspective-on-machine-learning" style="font-size: 80%;"><b>Perspective on Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs056.html#machine-learning-research" style="font-size: 80%;"><b>Machine Learning Research</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs057.html#starting-your-machine-learning-project" style="font-size: 80%;"><b>Starting your Machine Learning Project</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs058.html#choose-a-model-and-algorithm" style="font-size: 80%;"><b>Choose a Model and Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs059.html#preparing-your-data" style="font-size: 80%;"><b>Preparing Your Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs060.html#which-activation-and-weights-to-choose-in-neural-networks" style="font-size: 80%;"><b>Which Activation and Weights to Choose in Neural Networks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs061.html#optimization-methods-and-hyperparameters" style="font-size: 80%;"><b>Optimization Methods and Hyperparameters</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs062.html#resampling" style="font-size: 80%;"><b>Resampling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs063.html#other-courses-on-data-science-and-machine-learning-at-uio" style="font-size: 80%;"><b>Other courses on Data science and Machine Learning at UiO</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs064.html#additional-courses-of-interest" style="font-size: 80%;"><b>Additional courses of interest</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs065.html#what-s-the-future-like" style="font-size: 80%;"><b>What's the future like?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs066.html#types-of-machine-learning-a-repetition" style="font-size: 80%;"><b>Types of Machine Learning, a repetition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs067.html#why-boltzmann-machines" style="font-size: 80%;"><b>Why Boltzmann machines?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs068.html#boltzmann-machines" style="font-size: 80%;"><b>Boltzmann Machines</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs069.html#some-similarities-and-differences-from-dnns" style="font-size: 80%;"><b>Some similarities and differences from DNNs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs070.html#boltzmann-machines-bm" style="font-size: 80%;"><b>Boltzmann machines (BM)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs071.html#a-standard-bm-setup" style="font-size: 80%;"><b>A standard BM setup</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs072.html#the-structure-of-the-rbm-network" style="font-size: 80%;"><b>The structure of the RBM network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs073.html#the-network" style="font-size: 80%;"><b>The network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs074.html#goals" style="font-size: 80%;"><b>Goals</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs075.html#joint-distribution" style="font-size: 80%;"><b>Joint distribution</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs076.html#network-elements-the-energy-function" style="font-size: 80%;"><b>Network Elements, the energy function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs077.html#defining-different-types-of-rbms" style="font-size: 80%;"><b>Defining different types of RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs078.html#more-about-rbms" style="font-size: 80%;"><b>More about RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs079.html#autoencoders-overarching-view" style="font-size: 80%;"><b>Autoencoders: Overarching view</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs080.html#bayesian-machine-learning" style="font-size: 80%;"><b>Bayesian Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs081.html#reinforcement-learning" style="font-size: 80%;"><b>Reinforcement Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs082.html#transfer-learning" style="font-size: 80%;"><b>Transfer learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs083.html#adversarial-learning" style="font-size: 80%;"><b>Adversarial learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs084.html#dual-learning" style="font-size: 80%;"><b>Dual learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs085.html#distributed-machine-learning" style="font-size: 80%;"><b>Distributed machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs086.html#meta-learning" style="font-size: 80%;"><b>Meta learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs087.html#the-challenges-facing-machine-learning" style="font-size: 80%;"><b>The Challenges Facing Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="#explainable-machine-learning" style="font-size: 80%;"><b>Explainable machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs089.html#scientific-machine-learning" style="font-size: 80%;"><b>Scientific Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs090.html#quantum-machine-learning" style="font-size: 80%;"><b>Quantum machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs091.html#quantum-machine-learning-algorithms-based-on-linear-algebra" style="font-size: 80%;"><b>Quantum machine learning algorithms based on linear algebra</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs092.html#quantum-reinforcement-learning" style="font-size: 80%;"><b>Quantum reinforcement learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs093.html#quantum-deep-learning" style="font-size: 80%;"><b>Quantum deep learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs094.html#social-machine-learning" style="font-size: 80%;"><b>Social machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs095.html#the-last-words" style="font-size: 80%;"><b>The last words?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs096.html#ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean" style="font-size: 80%;"><b>AI/ML and some statements you may have heard (and what do they mean?)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs097.html#best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester" style="font-size: 80%;"><b>Best wishes to you all and thanks so much for your heroic efforts this semester</b></a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div> <!-- end of navigation bar -->
|
||||
<div class="container">
|
||||
<p> </p><p> </p><p> </p> <!-- add vertical space -->
|
||||
<a name="part0088"></a>
|
||||
<!-- !split -->
|
||||
<h2 id="explainable-machine-learning" 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>
|
||||
|
||||
<p><b>What is missing: the gap between correlation and causation</b>. Standard Machine Learning is based on what e have called a frequentist approach. </p>
|
||||
|
||||
<p>Most
|
||||
machine learning techniques, especially the statistical ones, depend
|
||||
highly on correlations in data sets 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>
|
||||
|
||||
<b>Bayesian Machine Learning is one of the exciting research directions in this field</b>.
|
||||
|
||||
<p>
|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
('Basic ideas of the Principal Component Analysis (PCA)',
|
||||
2,
|
||||
None,
|
||||
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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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|
||||
('Towards the PCA theorem', 2, None, 'towards-the-pca-theorem'),
|
||||
('More on the PCA Theorem', 2, None, 'more-on-the-pca-theorem'),
|
||||
("A kind of Bird's view on PCA",
|
||||
2,
|
||||
None,
|
||||
'a-kind-of-bird-s-view-on-pca'),
|
||||
('Writing our own PCA code', 2, None, 'writing-our-own-pca-code'),
|
||||
('Implementing it', 2, None, 'implementing-it'),
|
||||
('First Step', 2, None, 'first-step'),
|
||||
('Scaling', 2, None, 'scaling'),
|
||||
('Centered Data', 2, None, 'centered-data'),
|
||||
('Exploring', 2, None, 'exploring'),
|
||||
('Diagonalize the sample covariance matrix to obtain the '
|
||||
'principal components',
|
||||
2,
|
||||
None,
|
||||
'diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components'),
|
||||
('Collecting all Steps', 2, None, 'collecting-all-steps'),
|
||||
('Classical PCA Theorem', 2, None, 'classical-pca-theorem'),
|
||||
('The PCA Theorem', 2, None, 'the-pca-theorem'),
|
||||
('Geometric Interpretation and link with Singular Value '
|
||||
'Decomposition',
|
||||
2,
|
||||
None,
|
||||
'geometric-interpretation-and-link-with-singular-value-decomposition'),
|
||||
('PCA and scikit-learn', 2, None, 'pca-and-scikit-learn'),
|
||||
('Back to the Cancer Data', 2, None, 'back-to-the-cancer-data'),
|
||||
('Incremental PCA', 2, None, 'incremental-pca'),
|
||||
('Randomized PCA', 3, None, 'randomized-pca'),
|
||||
('Kernel PCA', 3, None, 'kernel-pca'),
|
||||
('Other techniques', 2, None, 'other-techniques'),
|
||||
('Clustering and Unsupervised Learning',
|
||||
2,
|
||||
None,
|
||||
'clustering-and-unsupervised-learning'),
|
||||
('Basic Idea of the $k$-means Clustering Algorithm',
|
||||
2,
|
||||
None,
|
||||
'basic-idea-of-the-k-means-clustering-algorithm'),
|
||||
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|
||||
('Basic Math of the $k$-means Algorithm',
|
||||
2,
|
||||
None,
|
||||
'basic-math-of-the-k-means-algorithm'),
|
||||
('Within Cluster Point Scatter',
|
||||
2,
|
||||
None,
|
||||
'within-cluster-point-scatter'),
|
||||
('More Details', 2, None, 'more-details'),
|
||||
('Total Cluster Variance', 2, None, 'total-cluster-variance'),
|
||||
('The $k$-means Clustering Algorithm',
|
||||
2,
|
||||
None,
|
||||
'the-k-means-clustering-algorithm'),
|
||||
('Summarizing', 2, None, 'summarizing'),
|
||||
('Writing our own Code, the Data Set',
|
||||
2,
|
||||
None,
|
||||
'writing-our-own-code-the-data-set'),
|
||||
('Implementing the $k$-means Algorithm',
|
||||
2,
|
||||
None,
|
||||
'implementing-the-k-means-algorithm'),
|
||||
('Plotting', 2, None, 'plotting'),
|
||||
('Continuing', 2, None, 'continuing'),
|
||||
('Wrapping it up', 2, None, 'wrapping-it-up'),
|
||||
('Summary of course', 2, None, 'summary-of-course'),
|
||||
('What? Me worry? No final exam in this course!',
|
||||
2,
|
||||
None,
|
||||
'what-me-worry-no-final-exam-in-this-course'),
|
||||
('What is the link between Artificial Intelligence and Machine '
|
||||
'Learning and some general Remarks',
|
||||
2,
|
||||
None,
|
||||
'what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks'),
|
||||
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|
||||
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|
||||
None,
|
||||
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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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|
||||
2,
|
||||
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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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|
||||
2,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
2,
|
||||
None,
|
||||
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|
||||
('Optimization Methods and Hyperparameters',
|
||||
2,
|
||||
None,
|
||||
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|
||||
('Resampling', 2, None, 'resampling'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
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|
||||
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|
||||
('Additional courses of interest',
|
||||
2,
|
||||
None,
|
||||
'additional-courses-of-interest'),
|
||||
("What's the future like?", 2, None, 'what-s-the-future-like'),
|
||||
('Types of Machine Learning, a repetition',
|
||||
2,
|
||||
None,
|
||||
'types-of-machine-learning-a-repetition'),
|
||||
('Why Boltzmann machines?', 2, None, 'why-boltzmann-machines'),
|
||||
('Boltzmann Machines', 2, None, 'boltzmann-machines'),
|
||||
('Some similarities and differences from DNNs',
|
||||
2,
|
||||
None,
|
||||
'some-similarities-and-differences-from-dnns'),
|
||||
('Boltzmann machines (BM)', 2, None, 'boltzmann-machines-bm'),
|
||||
('A standard BM setup', 2, None, 'a-standard-bm-setup'),
|
||||
('The structure of the RBM network',
|
||||
2,
|
||||
None,
|
||||
'the-structure-of-the-rbm-network'),
|
||||
('The network', 2, None, 'the-network'),
|
||||
('Goals', 2, None, 'goals'),
|
||||
('Joint distribution', 2, None, 'joint-distribution'),
|
||||
('Network Elements, the energy function',
|
||||
2,
|
||||
None,
|
||||
'network-elements-the-energy-function'),
|
||||
('Defining different types of RBMs',
|
||||
2,
|
||||
None,
|
||||
'defining-different-types-of-rbms'),
|
||||
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|
||||
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|
||||
2,
|
||||
None,
|
||||
'autoencoders-overarching-view'),
|
||||
('Bayesian Machine Learning',
|
||||
2,
|
||||
None,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('Dual learning', 2, None, 'dual-learning'),
|
||||
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|
||||
2,
|
||||
None,
|
||||
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|
||||
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|
||||
('The Challenges Facing Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'the-challenges-facing-machine-learning'),
|
||||
('Explainable machine learning',
|
||||
2,
|
||||
None,
|
||||
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|
||||
('Scientific Machine Learning',
|
||||
2,
|
||||
None,
|
||||
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|
||||
('Quantum machine learning', 2, None, 'quantum-machine-learning'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'quantum-machine-learning-algorithms-based-on-linear-algebra'),
|
||||
('Quantum reinforcement learning',
|
||||
2,
|
||||
None,
|
||||
'quantum-reinforcement-learning'),
|
||||
('Quantum deep learning', 2, None, 'quantum-deep-learning'),
|
||||
('Social machine learning', 2, None, 'social-machine-learning'),
|
||||
('The last words?', 2, None, 'the-last-words'),
|
||||
('AI/ML and some statements you may have heard (and what do they '
|
||||
'mean?)',
|
||||
2,
|
||||
None,
|
||||
'ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester')]}
|
||||
end of tocinfo -->
|
||||
|
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<a class="navbar-brand" href="week47-bs.html">Week 47: Unsupervised learning (PCA and Clustering) 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">
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs001.html#overview-of-week-47" style="font-size: 80%;"><b>Overview of week 47</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs002.html#basic-ideas-of-the-principal-component-analysis-pca" style="font-size: 80%;"><b>Basic ideas of the Principal Component Analysis (PCA)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs003.html#introducing-the-covariance-and-correlation-functions" style="font-size: 80%;"><b>Introducing the Covariance and Correlation functions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs004.html#more-on-the-covariance" style="font-size: 80%;"><b>More on the covariance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs005.html#reminding-ourselves-about-linear-regression" style="font-size: 80%;"><b>Reminding ourselves about Linear Regression</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs006.html#simple-example" style="font-size: 80%;"><b>Simple Example</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs007.html#the-correlation-matrix" style="font-size: 80%;"><b>The Correlation Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs008.html#numpy-functionality" style="font-size: 80%;"><b>Numpy Functionality</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs009.html#correlation-matrix-again" style="font-size: 80%;"><b>Correlation Matrix again</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs010.html#using-pandas" style="font-size: 80%;"><b>Using Pandas</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs011.html#and-then-the-franke-function" style="font-size: 80%;"><b>And then the Franke Function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs012.html#links-with-the-design-matrix" style="font-size: 80%;"><b>Links with the Design Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs013.html#computing-the-expectation-values" style="font-size: 80%;"><b>Computing the Expectation Values</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs014.html#towards-the-pca-theorem" style="font-size: 80%;"><b>Towards the PCA theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs015.html#more-on-the-pca-theorem" style="font-size: 80%;"><b>More on the PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs016.html#a-kind-of-bird-s-view-on-pca" style="font-size: 80%;"><b>A kind of Bird's view on PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs017.html#writing-our-own-pca-code" style="font-size: 80%;"><b>Writing our own PCA code</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs018.html#implementing-it" style="font-size: 80%;"><b>Implementing it</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs019.html#first-step" style="font-size: 80%;"><b>First Step</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs020.html#scaling" style="font-size: 80%;"><b>Scaling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs021.html#centered-data" style="font-size: 80%;"><b>Centered Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs022.html#exploring" style="font-size: 80%;"><b>Exploring</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs023.html#diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components" style="font-size: 80%;"><b>Diagonalize the sample covariance matrix to obtain the principal components</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs024.html#collecting-all-steps" style="font-size: 80%;"><b>Collecting all Steps</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs025.html#classical-pca-theorem" style="font-size: 80%;"><b>Classical PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs026.html#the-pca-theorem" style="font-size: 80%;"><b>The PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs027.html#geometric-interpretation-and-link-with-singular-value-decomposition" style="font-size: 80%;"><b>Geometric Interpretation and link with Singular Value Decomposition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs028.html#pca-and-scikit-learn" style="font-size: 80%;"><b>PCA and scikit-learn</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs029.html#back-to-the-cancer-data" style="font-size: 80%;"><b>Back to the Cancer Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#incremental-pca" style="font-size: 80%;"><b>Incremental PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#randomized-pca" style="font-size: 80%;"> Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#kernel-pca" style="font-size: 80%;"> Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs031.html#other-techniques" style="font-size: 80%;"><b>Other techniques</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs032.html#clustering-and-unsupervised-learning" style="font-size: 80%;"><b>Clustering and Unsupervised Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs033.html#basic-idea-of-the-k-means-clustering-algorithm" style="font-size: 80%;"><b>Basic Idea of the \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs034.html#the-k-means-algorithm" style="font-size: 80%;"><b>The \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs035.html#basic-math-of-the-k-means-algorithm" style="font-size: 80%;"><b>Basic Math of the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs036.html#within-cluster-point-scatter" style="font-size: 80%;"><b>Within Cluster Point Scatter</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs037.html#more-details" style="font-size: 80%;"><b>More Details</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs038.html#total-cluster-variance" style="font-size: 80%;"><b>Total Cluster Variance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs039.html#the-k-means-clustering-algorithm" style="font-size: 80%;"><b>The \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs040.html#summarizing" style="font-size: 80%;"><b>Summarizing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs041.html#writing-our-own-code-the-data-set" style="font-size: 80%;"><b>Writing our own Code, the Data Set</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs042.html#implementing-the-k-means-algorithm" style="font-size: 80%;"><b>Implementing the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs043.html#plotting" style="font-size: 80%;"><b>Plotting</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs044.html#continuing" style="font-size: 80%;"><b>Continuing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs045.html#wrapping-it-up" style="font-size: 80%;"><b>Wrapping it up</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs046.html#summary-of-course" style="font-size: 80%;"><b>Summary of course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs047.html#what-me-worry-no-final-exam-in-this-course" style="font-size: 80%;"><b>What? Me worry? No final exam in this course!</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs048.html#what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks" style="font-size: 80%;"><b>What is the link between Artificial Intelligence and Machine Learning and some general Remarks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs049.html#going-back-to-the-beginning-of-the-semester" style="font-size: 80%;"><b>Going back to the beginning of the semester</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs050.html#not-so-sharp-distinctions" style="font-size: 80%;"><b>Not so sharp distinctions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs051.html#topics-we-have-covered-this-year" style="font-size: 80%;"><b>Topics we have covered this year</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs052.html#statistical-analysis-and-optimization-of-data" style="font-size: 80%;"><b>Statistical analysis and optimization of data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs053.html#machine-learning" style="font-size: 80%;"><b>Machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs054.html#learning-outcomes-and-overarching-aims-of-this-course" style="font-size: 80%;"><b>Learning outcomes and overarching aims of this course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs055.html#perspective-on-machine-learning" style="font-size: 80%;"><b>Perspective on Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs056.html#machine-learning-research" style="font-size: 80%;"><b>Machine Learning Research</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs057.html#starting-your-machine-learning-project" style="font-size: 80%;"><b>Starting your Machine Learning Project</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs058.html#choose-a-model-and-algorithm" style="font-size: 80%;"><b>Choose a Model and Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs059.html#preparing-your-data" style="font-size: 80%;"><b>Preparing Your Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs060.html#which-activation-and-weights-to-choose-in-neural-networks" style="font-size: 80%;"><b>Which Activation and Weights to Choose in Neural Networks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs061.html#optimization-methods-and-hyperparameters" style="font-size: 80%;"><b>Optimization Methods and Hyperparameters</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs062.html#resampling" style="font-size: 80%;"><b>Resampling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs063.html#other-courses-on-data-science-and-machine-learning-at-uio" style="font-size: 80%;"><b>Other courses on Data science and Machine Learning at UiO</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs064.html#additional-courses-of-interest" style="font-size: 80%;"><b>Additional courses of interest</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs065.html#what-s-the-future-like" style="font-size: 80%;"><b>What's the future like?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs066.html#types-of-machine-learning-a-repetition" style="font-size: 80%;"><b>Types of Machine Learning, a repetition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs067.html#why-boltzmann-machines" style="font-size: 80%;"><b>Why Boltzmann machines?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs068.html#boltzmann-machines" style="font-size: 80%;"><b>Boltzmann Machines</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs069.html#some-similarities-and-differences-from-dnns" style="font-size: 80%;"><b>Some similarities and differences from DNNs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs070.html#boltzmann-machines-bm" style="font-size: 80%;"><b>Boltzmann machines (BM)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs071.html#a-standard-bm-setup" style="font-size: 80%;"><b>A standard BM setup</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs072.html#the-structure-of-the-rbm-network" style="font-size: 80%;"><b>The structure of the RBM network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs073.html#the-network" style="font-size: 80%;"><b>The network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs074.html#goals" style="font-size: 80%;"><b>Goals</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs075.html#joint-distribution" style="font-size: 80%;"><b>Joint distribution</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs076.html#network-elements-the-energy-function" style="font-size: 80%;"><b>Network Elements, the energy function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs077.html#defining-different-types-of-rbms" style="font-size: 80%;"><b>Defining different types of RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs078.html#more-about-rbms" style="font-size: 80%;"><b>More about RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs079.html#autoencoders-overarching-view" style="font-size: 80%;"><b>Autoencoders: Overarching view</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs080.html#bayesian-machine-learning" style="font-size: 80%;"><b>Bayesian Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs081.html#reinforcement-learning" style="font-size: 80%;"><b>Reinforcement Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs082.html#transfer-learning" style="font-size: 80%;"><b>Transfer learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs083.html#adversarial-learning" style="font-size: 80%;"><b>Adversarial learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs084.html#dual-learning" style="font-size: 80%;"><b>Dual learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs085.html#distributed-machine-learning" style="font-size: 80%;"><b>Distributed machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs086.html#meta-learning" style="font-size: 80%;"><b>Meta learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs087.html#the-challenges-facing-machine-learning" style="font-size: 80%;"><b>The Challenges Facing Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs088.html#explainable-machine-learning" style="font-size: 80%;"><b>Explainable machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="#scientific-machine-learning" style="font-size: 80%;"><b>Scientific Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs090.html#quantum-machine-learning" style="font-size: 80%;"><b>Quantum machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs091.html#quantum-machine-learning-algorithms-based-on-linear-algebra" style="font-size: 80%;"><b>Quantum machine learning algorithms based on linear algebra</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs092.html#quantum-reinforcement-learning" style="font-size: 80%;"><b>Quantum reinforcement learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs093.html#quantum-deep-learning" style="font-size: 80%;"><b>Quantum deep learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs094.html#social-machine-learning" style="font-size: 80%;"><b>Social machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs095.html#the-last-words" style="font-size: 80%;"><b>The last words?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs096.html#ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean" style="font-size: 80%;"><b>AI/ML and some statements you may have heard (and what do they mean?)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs097.html#best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester" style="font-size: 80%;"><b>Best wishes to you all and thanks so much for your heroic efforts this semester</b></a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div> <!-- end of navigation bar -->
|
||||
<div class="container">
|
||||
<p> </p><p> </p><p> </p> <!-- add vertical space -->
|
||||
<a name="part0089"></a>
|
||||
<!-- !split -->
|
||||
<h2 id="scientific-machine-learning" class="anchor">Scientific Machine Learning </h2>
|
||||
|
||||
<p>An important and emerging field is what has been dubbed as scientific ML, see the article by Deiana et al <a href="https://arxiv.org/abs/2110.13041" target="_self">Applications and Techniques for Fast Machine Learning in Science, arXiv:2110.13041</a></p>
|
||||
|
||||
<div class="panel panel-default">
|
||||
<div class="panel-body">
|
||||
<!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
|
||||
<p>The authors discuss applications and techniques for fast machine
|
||||
learning (ML) in science – the concept of integrating power ML
|
||||
methods into the real-time experimental data processing loop to
|
||||
accelerate scientific discovery. The report covers three main areas
|
||||
</p>
|
||||
|
||||
<ol>
|
||||
<li> applications for fast ML across a number of scientific domains;</li>
|
||||
<li> techniques for training and implementing performant and resource-efficient ML algorithms;</li>
|
||||
<li> and computing architectures, platforms, and technologies for deploying these algorithms.</li>
|
||||
</ol>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
||||
<p>
|
||||
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<li class="active"><a href="._week47-bs089.html">90</a></li>
|
||||
<li><a href="._week47-bs090.html">91</a></li>
|
||||
<li><a href="._week47-bs091.html">92</a></li>
|
||||
<li><a href="._week47-bs092.html">93</a></li>
|
||||
<li><a href="._week47-bs093.html">94</a></li>
|
||||
<li><a href="._week47-bs094.html">95</a></li>
|
||||
<li><a href="._week47-bs095.html">96</a></li>
|
||||
<li><a href="._week47-bs096.html">97</a></li>
|
||||
<li><a href="._week47-bs097.html">98</a></li>
|
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<li><a href="._week47-bs090.html">»</a></li>
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|
||||
('Basic ideas of the Principal Component Analysis (PCA)',
|
||||
2,
|
||||
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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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|
||||
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||||
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('Other courses on Data science and Machine Learning at UiO',
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||||
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||||
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||||
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||||
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||||
("What's the future like?", 2, None, 'what-s-the-future-like'),
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||||
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||||
2,
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||||
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||||
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||||
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||||
('Boltzmann Machines', 2, None, 'boltzmann-machines'),
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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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|
||||
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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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|
||||
2,
|
||||
None,
|
||||
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|
||||
('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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<a class="navbar-brand" href="week47-bs.html">Week 47: Unsupervised learning (PCA and Clustering) and Summary of Course</a>
|
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|
||||
<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
|
||||
<ul class="dropdown-menu">
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs001.html#overview-of-week-47" style="font-size: 80%;"><b>Overview of week 47</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs002.html#basic-ideas-of-the-principal-component-analysis-pca" style="font-size: 80%;"><b>Basic ideas of the Principal Component Analysis (PCA)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs003.html#introducing-the-covariance-and-correlation-functions" style="font-size: 80%;"><b>Introducing the Covariance and Correlation functions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs004.html#more-on-the-covariance" style="font-size: 80%;"><b>More on the covariance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs005.html#reminding-ourselves-about-linear-regression" style="font-size: 80%;"><b>Reminding ourselves about Linear Regression</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs006.html#simple-example" style="font-size: 80%;"><b>Simple Example</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs007.html#the-correlation-matrix" style="font-size: 80%;"><b>The Correlation Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs008.html#numpy-functionality" style="font-size: 80%;"><b>Numpy Functionality</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs009.html#correlation-matrix-again" style="font-size: 80%;"><b>Correlation Matrix again</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs010.html#using-pandas" style="font-size: 80%;"><b>Using Pandas</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs011.html#and-then-the-franke-function" style="font-size: 80%;"><b>And then the Franke Function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs012.html#links-with-the-design-matrix" style="font-size: 80%;"><b>Links with the Design Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs013.html#computing-the-expectation-values" style="font-size: 80%;"><b>Computing the Expectation Values</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs014.html#towards-the-pca-theorem" style="font-size: 80%;"><b>Towards the PCA theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs015.html#more-on-the-pca-theorem" style="font-size: 80%;"><b>More on the PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs016.html#a-kind-of-bird-s-view-on-pca" style="font-size: 80%;"><b>A kind of Bird's view on PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs017.html#writing-our-own-pca-code" style="font-size: 80%;"><b>Writing our own PCA code</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs018.html#implementing-it" style="font-size: 80%;"><b>Implementing it</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs019.html#first-step" style="font-size: 80%;"><b>First Step</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs020.html#scaling" style="font-size: 80%;"><b>Scaling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs021.html#centered-data" style="font-size: 80%;"><b>Centered Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs022.html#exploring" style="font-size: 80%;"><b>Exploring</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs023.html#diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components" style="font-size: 80%;"><b>Diagonalize the sample covariance matrix to obtain the principal components</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs024.html#collecting-all-steps" style="font-size: 80%;"><b>Collecting all Steps</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs025.html#classical-pca-theorem" style="font-size: 80%;"><b>Classical PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs026.html#the-pca-theorem" style="font-size: 80%;"><b>The PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs027.html#geometric-interpretation-and-link-with-singular-value-decomposition" style="font-size: 80%;"><b>Geometric Interpretation and link with Singular Value Decomposition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs028.html#pca-and-scikit-learn" style="font-size: 80%;"><b>PCA and scikit-learn</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs029.html#back-to-the-cancer-data" style="font-size: 80%;"><b>Back to the Cancer Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#incremental-pca" style="font-size: 80%;"><b>Incremental PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#randomized-pca" style="font-size: 80%;"> Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#kernel-pca" style="font-size: 80%;"> Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs031.html#other-techniques" style="font-size: 80%;"><b>Other techniques</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs032.html#clustering-and-unsupervised-learning" style="font-size: 80%;"><b>Clustering and Unsupervised Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs033.html#basic-idea-of-the-k-means-clustering-algorithm" style="font-size: 80%;"><b>Basic Idea of the \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs034.html#the-k-means-algorithm" style="font-size: 80%;"><b>The \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs035.html#basic-math-of-the-k-means-algorithm" style="font-size: 80%;"><b>Basic Math of the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs036.html#within-cluster-point-scatter" style="font-size: 80%;"><b>Within Cluster Point Scatter</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs037.html#more-details" style="font-size: 80%;"><b>More Details</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs038.html#total-cluster-variance" style="font-size: 80%;"><b>Total Cluster Variance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs039.html#the-k-means-clustering-algorithm" style="font-size: 80%;"><b>The \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs040.html#summarizing" style="font-size: 80%;"><b>Summarizing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs041.html#writing-our-own-code-the-data-set" style="font-size: 80%;"><b>Writing our own Code, the Data Set</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs042.html#implementing-the-k-means-algorithm" style="font-size: 80%;"><b>Implementing the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs043.html#plotting" style="font-size: 80%;"><b>Plotting</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs044.html#continuing" style="font-size: 80%;"><b>Continuing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs045.html#wrapping-it-up" style="font-size: 80%;"><b>Wrapping it up</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs046.html#summary-of-course" style="font-size: 80%;"><b>Summary of course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs047.html#what-me-worry-no-final-exam-in-this-course" style="font-size: 80%;"><b>What? Me worry? No final exam in this course!</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs048.html#what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks" style="font-size: 80%;"><b>What is the link between Artificial Intelligence and Machine Learning and some general Remarks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs049.html#going-back-to-the-beginning-of-the-semester" style="font-size: 80%;"><b>Going back to the beginning of the semester</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs050.html#not-so-sharp-distinctions" style="font-size: 80%;"><b>Not so sharp distinctions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs051.html#topics-we-have-covered-this-year" style="font-size: 80%;"><b>Topics we have covered this year</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs052.html#statistical-analysis-and-optimization-of-data" style="font-size: 80%;"><b>Statistical analysis and optimization of data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs053.html#machine-learning" style="font-size: 80%;"><b>Machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs054.html#learning-outcomes-and-overarching-aims-of-this-course" style="font-size: 80%;"><b>Learning outcomes and overarching aims of this course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs055.html#perspective-on-machine-learning" style="font-size: 80%;"><b>Perspective on Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs056.html#machine-learning-research" style="font-size: 80%;"><b>Machine Learning Research</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs057.html#starting-your-machine-learning-project" style="font-size: 80%;"><b>Starting your Machine Learning Project</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs058.html#choose-a-model-and-algorithm" style="font-size: 80%;"><b>Choose a Model and Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs059.html#preparing-your-data" style="font-size: 80%;"><b>Preparing Your Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs060.html#which-activation-and-weights-to-choose-in-neural-networks" style="font-size: 80%;"><b>Which Activation and Weights to Choose in Neural Networks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs061.html#optimization-methods-and-hyperparameters" style="font-size: 80%;"><b>Optimization Methods and Hyperparameters</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs062.html#resampling" style="font-size: 80%;"><b>Resampling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs063.html#other-courses-on-data-science-and-machine-learning-at-uio" style="font-size: 80%;"><b>Other courses on Data science and Machine Learning at UiO</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs064.html#additional-courses-of-interest" style="font-size: 80%;"><b>Additional courses of interest</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs065.html#what-s-the-future-like" style="font-size: 80%;"><b>What's the future like?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs066.html#types-of-machine-learning-a-repetition" style="font-size: 80%;"><b>Types of Machine Learning, a repetition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs067.html#why-boltzmann-machines" style="font-size: 80%;"><b>Why Boltzmann machines?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs068.html#boltzmann-machines" style="font-size: 80%;"><b>Boltzmann Machines</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs069.html#some-similarities-and-differences-from-dnns" style="font-size: 80%;"><b>Some similarities and differences from DNNs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs070.html#boltzmann-machines-bm" style="font-size: 80%;"><b>Boltzmann machines (BM)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs071.html#a-standard-bm-setup" style="font-size: 80%;"><b>A standard BM setup</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs072.html#the-structure-of-the-rbm-network" style="font-size: 80%;"><b>The structure of the RBM network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs073.html#the-network" style="font-size: 80%;"><b>The network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs074.html#goals" style="font-size: 80%;"><b>Goals</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs075.html#joint-distribution" style="font-size: 80%;"><b>Joint distribution</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs076.html#network-elements-the-energy-function" style="font-size: 80%;"><b>Network Elements, the energy function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs077.html#defining-different-types-of-rbms" style="font-size: 80%;"><b>Defining different types of RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs078.html#more-about-rbms" style="font-size: 80%;"><b>More about RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs079.html#autoencoders-overarching-view" style="font-size: 80%;"><b>Autoencoders: Overarching view</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs080.html#bayesian-machine-learning" style="font-size: 80%;"><b>Bayesian Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs081.html#reinforcement-learning" style="font-size: 80%;"><b>Reinforcement Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs082.html#transfer-learning" style="font-size: 80%;"><b>Transfer learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs083.html#adversarial-learning" style="font-size: 80%;"><b>Adversarial learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs084.html#dual-learning" style="font-size: 80%;"><b>Dual learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs085.html#distributed-machine-learning" style="font-size: 80%;"><b>Distributed machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs086.html#meta-learning" style="font-size: 80%;"><b>Meta learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs087.html#the-challenges-facing-machine-learning" style="font-size: 80%;"><b>The Challenges Facing Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs088.html#explainable-machine-learning" style="font-size: 80%;"><b>Explainable machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs089.html#scientific-machine-learning" style="font-size: 80%;"><b>Scientific Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="#quantum-machine-learning" style="font-size: 80%;"><b>Quantum machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs091.html#quantum-machine-learning-algorithms-based-on-linear-algebra" style="font-size: 80%;"><b>Quantum machine learning algorithms based on linear algebra</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs092.html#quantum-reinforcement-learning" style="font-size: 80%;"><b>Quantum reinforcement learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs093.html#quantum-deep-learning" style="font-size: 80%;"><b>Quantum deep learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs094.html#social-machine-learning" style="font-size: 80%;"><b>Social machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs095.html#the-last-words" style="font-size: 80%;"><b>The last words?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs096.html#ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean" style="font-size: 80%;"><b>AI/ML and some statements you may have heard (and what do they mean?)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs097.html#best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester" style="font-size: 80%;"><b>Best wishes to you all and thanks so much for your heroic efforts this semester</b></a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
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|
||||
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|
||||
<p> </p><p> </p><p> </p> <!-- add vertical space -->
|
||||
<a name="part0090"></a>
|
||||
<!-- !split -->
|
||||
<h2 id="quantum-machine-learning" 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>
|
||||
|
||||
<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>
|
||||
|
||||
<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>
|
||||
|
||||
<p><a href="https://www.youtube.com/watch?v=Xh9pUu3-WxM&ab_channel=InstituteforPure%26AppliedMathematics%28IPAM%29" target="_self">Lecture on Quantum ML</a>.</p>
|
||||
|
||||
<p><a href="https://physics.aps.org/articles/v13/179?utm_campaign=weekly&utm_medium=email&utm_source=emailalert" target="_self">Read interview with Maria Schuld on her work on Quantum Machine Learning</a>. See also <a href="https://www.springer.com/gp/book/9783319964232" target="_self">her recent textbook</a>. </p>
|
||||
|
||||
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|
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|
||||
('Back to the Cancer Data', 2, None, 'back-to-the-cancer-data'),
|
||||
('Incremental PCA', 2, None, 'incremental-pca'),
|
||||
('Randomized PCA', 3, None, 'randomized-pca'),
|
||||
('Kernel PCA', 3, None, 'kernel-pca'),
|
||||
('Other techniques', 2, None, 'other-techniques'),
|
||||
('Clustering and Unsupervised Learning',
|
||||
2,
|
||||
None,
|
||||
'clustering-and-unsupervised-learning'),
|
||||
('Basic Idea of the $k$-means Clustering Algorithm',
|
||||
2,
|
||||
None,
|
||||
'basic-idea-of-the-k-means-clustering-algorithm'),
|
||||
('The $k$-means Algorithm', 2, None, 'the-k-means-algorithm'),
|
||||
('Basic Math of the $k$-means Algorithm',
|
||||
2,
|
||||
None,
|
||||
'basic-math-of-the-k-means-algorithm'),
|
||||
('Within Cluster Point Scatter',
|
||||
2,
|
||||
None,
|
||||
'within-cluster-point-scatter'),
|
||||
('More Details', 2, None, 'more-details'),
|
||||
('Total Cluster Variance', 2, None, 'total-cluster-variance'),
|
||||
('The $k$-means Clustering Algorithm',
|
||||
2,
|
||||
None,
|
||||
'the-k-means-clustering-algorithm'),
|
||||
('Summarizing', 2, None, 'summarizing'),
|
||||
('Writing our own Code, the Data Set',
|
||||
2,
|
||||
None,
|
||||
'writing-our-own-code-the-data-set'),
|
||||
('Implementing the $k$-means Algorithm',
|
||||
2,
|
||||
None,
|
||||
'implementing-the-k-means-algorithm'),
|
||||
('Plotting', 2, None, 'plotting'),
|
||||
('Continuing', 2, None, 'continuing'),
|
||||
('Wrapping it up', 2, None, 'wrapping-it-up'),
|
||||
('Summary of course', 2, None, 'summary-of-course'),
|
||||
('What? Me worry? No final exam in this course!',
|
||||
2,
|
||||
None,
|
||||
'what-me-worry-no-final-exam-in-this-course'),
|
||||
('What is the link between Artificial Intelligence and Machine '
|
||||
'Learning and some general Remarks',
|
||||
2,
|
||||
None,
|
||||
'what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks'),
|
||||
('Going back to the beginning of the semester',
|
||||
2,
|
||||
None,
|
||||
'going-back-to-the-beginning-of-the-semester'),
|
||||
('Not so sharp distinctions',
|
||||
2,
|
||||
None,
|
||||
'not-so-sharp-distinctions'),
|
||||
('Topics we have covered this year',
|
||||
2,
|
||||
None,
|
||||
'topics-we-have-covered-this-year'),
|
||||
('Statistical analysis and optimization of data',
|
||||
2,
|
||||
None,
|
||||
'statistical-analysis-and-optimization-of-data'),
|
||||
('Machine learning', 2, None, 'machine-learning'),
|
||||
('Learning outcomes and overarching aims of this course',
|
||||
2,
|
||||
None,
|
||||
'learning-outcomes-and-overarching-aims-of-this-course'),
|
||||
('Perspective on Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'perspective-on-machine-learning'),
|
||||
('Machine Learning Research',
|
||||
2,
|
||||
None,
|
||||
'machine-learning-research'),
|
||||
('Starting your Machine Learning Project',
|
||||
2,
|
||||
None,
|
||||
'starting-your-machine-learning-project'),
|
||||
('Choose a Model and Algorithm',
|
||||
2,
|
||||
None,
|
||||
'choose-a-model-and-algorithm'),
|
||||
('Preparing Your Data', 2, None, 'preparing-your-data'),
|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
||||
2,
|
||||
None,
|
||||
'which-activation-and-weights-to-choose-in-neural-networks'),
|
||||
('Optimization Methods and Hyperparameters',
|
||||
2,
|
||||
None,
|
||||
'optimization-methods-and-hyperparameters'),
|
||||
('Resampling', 2, None, 'resampling'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
||||
'other-courses-on-data-science-and-machine-learning-at-uio'),
|
||||
('Additional courses of interest',
|
||||
2,
|
||||
None,
|
||||
'additional-courses-of-interest'),
|
||||
("What's the future like?", 2, None, 'what-s-the-future-like'),
|
||||
('Types of Machine Learning, a repetition',
|
||||
2,
|
||||
None,
|
||||
'types-of-machine-learning-a-repetition'),
|
||||
('Why Boltzmann machines?', 2, None, 'why-boltzmann-machines'),
|
||||
('Boltzmann Machines', 2, None, 'boltzmann-machines'),
|
||||
('Some similarities and differences from DNNs',
|
||||
2,
|
||||
None,
|
||||
'some-similarities-and-differences-from-dnns'),
|
||||
('Boltzmann machines (BM)', 2, None, 'boltzmann-machines-bm'),
|
||||
('A standard BM setup', 2, None, 'a-standard-bm-setup'),
|
||||
('The structure of the RBM network',
|
||||
2,
|
||||
None,
|
||||
'the-structure-of-the-rbm-network'),
|
||||
('The network', 2, None, 'the-network'),
|
||||
('Goals', 2, None, 'goals'),
|
||||
('Joint distribution', 2, None, 'joint-distribution'),
|
||||
('Network Elements, the energy function',
|
||||
2,
|
||||
None,
|
||||
'network-elements-the-energy-function'),
|
||||
('Defining different types of RBMs',
|
||||
2,
|
||||
None,
|
||||
'defining-different-types-of-rbms'),
|
||||
('More about RBMs', 2, None, 'more-about-rbms'),
|
||||
('Autoencoders: Overarching view',
|
||||
2,
|
||||
None,
|
||||
'autoencoders-overarching-view'),
|
||||
('Bayesian Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'bayesian-machine-learning'),
|
||||
('Reinforcement Learning', 2, None, 'reinforcement-learning'),
|
||||
('Transfer learning', 2, None, 'transfer-learning'),
|
||||
('Adversarial learning', 2, None, 'adversarial-learning'),
|
||||
('Dual learning', 2, None, 'dual-learning'),
|
||||
('Distributed machine learning',
|
||||
2,
|
||||
None,
|
||||
'distributed-machine-learning'),
|
||||
('Meta learning', 2, None, 'meta-learning'),
|
||||
('The Challenges Facing Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'the-challenges-facing-machine-learning'),
|
||||
('Explainable machine learning',
|
||||
2,
|
||||
None,
|
||||
'explainable-machine-learning'),
|
||||
('Scientific Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'scientific-machine-learning'),
|
||||
('Quantum machine learning', 2, None, 'quantum-machine-learning'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'quantum-machine-learning-algorithms-based-on-linear-algebra'),
|
||||
('Quantum reinforcement learning',
|
||||
2,
|
||||
None,
|
||||
'quantum-reinforcement-learning'),
|
||||
('Quantum deep learning', 2, None, 'quantum-deep-learning'),
|
||||
('Social machine learning', 2, None, 'social-machine-learning'),
|
||||
('The last words?', 2, None, 'the-last-words'),
|
||||
('AI/ML and some statements you may have heard (and what do they '
|
||||
'mean?)',
|
||||
2,
|
||||
None,
|
||||
'ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester')]}
|
||||
end of tocinfo -->
|
||||
|
||||
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|
||||
|
||||
|
||||
|
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|
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<a class="navbar-brand" href="week47-bs.html">Week 47: Unsupervised learning (PCA and Clustering) and Summary of Course</a>
|
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|
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|
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<ul class="nav navbar-nav navbar-right">
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|
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<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
|
||||
<ul class="dropdown-menu">
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs001.html#overview-of-week-47" style="font-size: 80%;"><b>Overview of week 47</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs002.html#basic-ideas-of-the-principal-component-analysis-pca" style="font-size: 80%;"><b>Basic ideas of the Principal Component Analysis (PCA)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs003.html#introducing-the-covariance-and-correlation-functions" style="font-size: 80%;"><b>Introducing the Covariance and Correlation functions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs004.html#more-on-the-covariance" style="font-size: 80%;"><b>More on the covariance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs005.html#reminding-ourselves-about-linear-regression" style="font-size: 80%;"><b>Reminding ourselves about Linear Regression</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs006.html#simple-example" style="font-size: 80%;"><b>Simple Example</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs007.html#the-correlation-matrix" style="font-size: 80%;"><b>The Correlation Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs008.html#numpy-functionality" style="font-size: 80%;"><b>Numpy Functionality</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs009.html#correlation-matrix-again" style="font-size: 80%;"><b>Correlation Matrix again</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs010.html#using-pandas" style="font-size: 80%;"><b>Using Pandas</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs011.html#and-then-the-franke-function" style="font-size: 80%;"><b>And then the Franke Function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs012.html#links-with-the-design-matrix" style="font-size: 80%;"><b>Links with the Design Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs013.html#computing-the-expectation-values" style="font-size: 80%;"><b>Computing the Expectation Values</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs014.html#towards-the-pca-theorem" style="font-size: 80%;"><b>Towards the PCA theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs015.html#more-on-the-pca-theorem" style="font-size: 80%;"><b>More on the PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs016.html#a-kind-of-bird-s-view-on-pca" style="font-size: 80%;"><b>A kind of Bird's view on PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs017.html#writing-our-own-pca-code" style="font-size: 80%;"><b>Writing our own PCA code</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs018.html#implementing-it" style="font-size: 80%;"><b>Implementing it</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs019.html#first-step" style="font-size: 80%;"><b>First Step</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs020.html#scaling" style="font-size: 80%;"><b>Scaling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs021.html#centered-data" style="font-size: 80%;"><b>Centered Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs022.html#exploring" style="font-size: 80%;"><b>Exploring</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs023.html#diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components" style="font-size: 80%;"><b>Diagonalize the sample covariance matrix to obtain the principal components</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs024.html#collecting-all-steps" style="font-size: 80%;"><b>Collecting all Steps</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs025.html#classical-pca-theorem" style="font-size: 80%;"><b>Classical PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs026.html#the-pca-theorem" style="font-size: 80%;"><b>The PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs027.html#geometric-interpretation-and-link-with-singular-value-decomposition" style="font-size: 80%;"><b>Geometric Interpretation and link with Singular Value Decomposition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs028.html#pca-and-scikit-learn" style="font-size: 80%;"><b>PCA and scikit-learn</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs029.html#back-to-the-cancer-data" style="font-size: 80%;"><b>Back to the Cancer Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#incremental-pca" style="font-size: 80%;"><b>Incremental PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#randomized-pca" style="font-size: 80%;"> Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#kernel-pca" style="font-size: 80%;"> Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs031.html#other-techniques" style="font-size: 80%;"><b>Other techniques</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs032.html#clustering-and-unsupervised-learning" style="font-size: 80%;"><b>Clustering and Unsupervised Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs033.html#basic-idea-of-the-k-means-clustering-algorithm" style="font-size: 80%;"><b>Basic Idea of the \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs034.html#the-k-means-algorithm" style="font-size: 80%;"><b>The \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs035.html#basic-math-of-the-k-means-algorithm" style="font-size: 80%;"><b>Basic Math of the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs036.html#within-cluster-point-scatter" style="font-size: 80%;"><b>Within Cluster Point Scatter</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs037.html#more-details" style="font-size: 80%;"><b>More Details</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs038.html#total-cluster-variance" style="font-size: 80%;"><b>Total Cluster Variance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs039.html#the-k-means-clustering-algorithm" style="font-size: 80%;"><b>The \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs040.html#summarizing" style="font-size: 80%;"><b>Summarizing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs041.html#writing-our-own-code-the-data-set" style="font-size: 80%;"><b>Writing our own Code, the Data Set</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs042.html#implementing-the-k-means-algorithm" style="font-size: 80%;"><b>Implementing the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs043.html#plotting" style="font-size: 80%;"><b>Plotting</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs044.html#continuing" style="font-size: 80%;"><b>Continuing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs045.html#wrapping-it-up" style="font-size: 80%;"><b>Wrapping it up</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs046.html#summary-of-course" style="font-size: 80%;"><b>Summary of course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs047.html#what-me-worry-no-final-exam-in-this-course" style="font-size: 80%;"><b>What? Me worry? No final exam in this course!</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs048.html#what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks" style="font-size: 80%;"><b>What is the link between Artificial Intelligence and Machine Learning and some general Remarks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs049.html#going-back-to-the-beginning-of-the-semester" style="font-size: 80%;"><b>Going back to the beginning of the semester</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs050.html#not-so-sharp-distinctions" style="font-size: 80%;"><b>Not so sharp distinctions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs051.html#topics-we-have-covered-this-year" style="font-size: 80%;"><b>Topics we have covered this year</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs052.html#statistical-analysis-and-optimization-of-data" style="font-size: 80%;"><b>Statistical analysis and optimization of data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs053.html#machine-learning" style="font-size: 80%;"><b>Machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs054.html#learning-outcomes-and-overarching-aims-of-this-course" style="font-size: 80%;"><b>Learning outcomes and overarching aims of this course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs055.html#perspective-on-machine-learning" style="font-size: 80%;"><b>Perspective on Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs056.html#machine-learning-research" style="font-size: 80%;"><b>Machine Learning Research</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs057.html#starting-your-machine-learning-project" style="font-size: 80%;"><b>Starting your Machine Learning Project</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs058.html#choose-a-model-and-algorithm" style="font-size: 80%;"><b>Choose a Model and Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs059.html#preparing-your-data" style="font-size: 80%;"><b>Preparing Your Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs060.html#which-activation-and-weights-to-choose-in-neural-networks" style="font-size: 80%;"><b>Which Activation and Weights to Choose in Neural Networks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs061.html#optimization-methods-and-hyperparameters" style="font-size: 80%;"><b>Optimization Methods and Hyperparameters</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs062.html#resampling" style="font-size: 80%;"><b>Resampling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs063.html#other-courses-on-data-science-and-machine-learning-at-uio" style="font-size: 80%;"><b>Other courses on Data science and Machine Learning at UiO</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs064.html#additional-courses-of-interest" style="font-size: 80%;"><b>Additional courses of interest</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs065.html#what-s-the-future-like" style="font-size: 80%;"><b>What's the future like?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs066.html#types-of-machine-learning-a-repetition" style="font-size: 80%;"><b>Types of Machine Learning, a repetition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs067.html#why-boltzmann-machines" style="font-size: 80%;"><b>Why Boltzmann machines?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs068.html#boltzmann-machines" style="font-size: 80%;"><b>Boltzmann Machines</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs069.html#some-similarities-and-differences-from-dnns" style="font-size: 80%;"><b>Some similarities and differences from DNNs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs070.html#boltzmann-machines-bm" style="font-size: 80%;"><b>Boltzmann machines (BM)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs071.html#a-standard-bm-setup" style="font-size: 80%;"><b>A standard BM setup</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs072.html#the-structure-of-the-rbm-network" style="font-size: 80%;"><b>The structure of the RBM network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs073.html#the-network" style="font-size: 80%;"><b>The network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs074.html#goals" style="font-size: 80%;"><b>Goals</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs075.html#joint-distribution" style="font-size: 80%;"><b>Joint distribution</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs076.html#network-elements-the-energy-function" style="font-size: 80%;"><b>Network Elements, the energy function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs077.html#defining-different-types-of-rbms" style="font-size: 80%;"><b>Defining different types of RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs078.html#more-about-rbms" style="font-size: 80%;"><b>More about RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs079.html#autoencoders-overarching-view" style="font-size: 80%;"><b>Autoencoders: Overarching view</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs080.html#bayesian-machine-learning" style="font-size: 80%;"><b>Bayesian Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs081.html#reinforcement-learning" style="font-size: 80%;"><b>Reinforcement Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs082.html#transfer-learning" style="font-size: 80%;"><b>Transfer learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs083.html#adversarial-learning" style="font-size: 80%;"><b>Adversarial learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs084.html#dual-learning" style="font-size: 80%;"><b>Dual learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs085.html#distributed-machine-learning" style="font-size: 80%;"><b>Distributed machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs086.html#meta-learning" style="font-size: 80%;"><b>Meta learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs087.html#the-challenges-facing-machine-learning" style="font-size: 80%;"><b>The Challenges Facing Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs088.html#explainable-machine-learning" style="font-size: 80%;"><b>Explainable machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs089.html#scientific-machine-learning" style="font-size: 80%;"><b>Scientific Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs090.html#quantum-machine-learning" style="font-size: 80%;"><b>Quantum machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="#quantum-machine-learning-algorithms-based-on-linear-algebra" style="font-size: 80%;"><b>Quantum machine learning algorithms based on linear algebra</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs092.html#quantum-reinforcement-learning" style="font-size: 80%;"><b>Quantum reinforcement learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs093.html#quantum-deep-learning" style="font-size: 80%;"><b>Quantum deep learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs094.html#social-machine-learning" style="font-size: 80%;"><b>Social machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs095.html#the-last-words" style="font-size: 80%;"><b>The last words?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs096.html#ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean" style="font-size: 80%;"><b>AI/ML and some statements you may have heard (and what do they mean?)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs097.html#best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester" style="font-size: 80%;"><b>Best wishes to you all and thanks so much for your heroic efforts this semester</b></a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div> <!-- end of navigation bar -->
|
||||
<div class="container">
|
||||
<p> </p><p> </p><p> </p> <!-- add vertical space -->
|
||||
<a name="part0091"></a>
|
||||
<!-- !split -->
|
||||
<h2 id="quantum-machine-learning-algorithms-based-on-linear-algebra" 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.
|
||||
</p>
|
||||
|
||||
<p>
|
||||
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|
||||
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|
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|
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|
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|
||||
{'highest level': 2,
|
||||
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|
||||
('Basic ideas of the Principal Component Analysis (PCA)',
|
||||
2,
|
||||
None,
|
||||
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|
||||
('Introducing the Covariance and Correlation functions',
|
||||
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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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|
||||
('Computing the Expectation Values',
|
||||
2,
|
||||
None,
|
||||
'computing-the-expectation-values'),
|
||||
('Towards the PCA theorem', 2, None, 'towards-the-pca-theorem'),
|
||||
('More on the PCA Theorem', 2, None, 'more-on-the-pca-theorem'),
|
||||
("A kind of Bird's view on PCA",
|
||||
2,
|
||||
None,
|
||||
'a-kind-of-bird-s-view-on-pca'),
|
||||
('Writing our own PCA code', 2, None, 'writing-our-own-pca-code'),
|
||||
('Implementing it', 2, None, 'implementing-it'),
|
||||
('First Step', 2, None, 'first-step'),
|
||||
('Scaling', 2, None, 'scaling'),
|
||||
('Centered Data', 2, None, 'centered-data'),
|
||||
('Exploring', 2, None, 'exploring'),
|
||||
('Diagonalize the sample covariance matrix to obtain the '
|
||||
'principal components',
|
||||
2,
|
||||
None,
|
||||
'diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components'),
|
||||
('Collecting all Steps', 2, None, 'collecting-all-steps'),
|
||||
('Classical PCA Theorem', 2, None, 'classical-pca-theorem'),
|
||||
('The PCA Theorem', 2, None, 'the-pca-theorem'),
|
||||
('Geometric Interpretation and link with Singular Value '
|
||||
'Decomposition',
|
||||
2,
|
||||
None,
|
||||
'geometric-interpretation-and-link-with-singular-value-decomposition'),
|
||||
('PCA and scikit-learn', 2, None, 'pca-and-scikit-learn'),
|
||||
('Back to the Cancer Data', 2, None, 'back-to-the-cancer-data'),
|
||||
('Incremental PCA', 2, None, 'incremental-pca'),
|
||||
('Randomized PCA', 3, None, 'randomized-pca'),
|
||||
('Kernel PCA', 3, None, 'kernel-pca'),
|
||||
('Other techniques', 2, None, 'other-techniques'),
|
||||
('Clustering and Unsupervised Learning',
|
||||
2,
|
||||
None,
|
||||
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|
||||
('Basic Idea of the $k$-means Clustering Algorithm',
|
||||
2,
|
||||
None,
|
||||
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|
||||
('The $k$-means Algorithm', 2, None, 'the-k-means-algorithm'),
|
||||
('Basic Math of the $k$-means Algorithm',
|
||||
2,
|
||||
None,
|
||||
'basic-math-of-the-k-means-algorithm'),
|
||||
('Within Cluster Point Scatter',
|
||||
2,
|
||||
None,
|
||||
'within-cluster-point-scatter'),
|
||||
('More Details', 2, None, 'more-details'),
|
||||
('Total Cluster Variance', 2, None, 'total-cluster-variance'),
|
||||
('The $k$-means Clustering Algorithm',
|
||||
2,
|
||||
None,
|
||||
'the-k-means-clustering-algorithm'),
|
||||
('Summarizing', 2, None, 'summarizing'),
|
||||
('Writing our own Code, the Data Set',
|
||||
2,
|
||||
None,
|
||||
'writing-our-own-code-the-data-set'),
|
||||
('Implementing the $k$-means Algorithm',
|
||||
2,
|
||||
None,
|
||||
'implementing-the-k-means-algorithm'),
|
||||
('Plotting', 2, None, 'plotting'),
|
||||
('Continuing', 2, None, 'continuing'),
|
||||
('Wrapping it up', 2, None, 'wrapping-it-up'),
|
||||
('Summary of course', 2, None, 'summary-of-course'),
|
||||
('What? Me worry? No final exam in this course!',
|
||||
2,
|
||||
None,
|
||||
'what-me-worry-no-final-exam-in-this-course'),
|
||||
('What is the link between Artificial Intelligence and Machine '
|
||||
'Learning and some general Remarks',
|
||||
2,
|
||||
None,
|
||||
'what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks'),
|
||||
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|
||||
2,
|
||||
None,
|
||||
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|
||||
('Not so sharp distinctions',
|
||||
2,
|
||||
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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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|
||||
2,
|
||||
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|
||||
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|
||||
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|
||||
2,
|
||||
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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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|
||||
2,
|
||||
None,
|
||||
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|
||||
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|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
||||
2,
|
||||
None,
|
||||
'which-activation-and-weights-to-choose-in-neural-networks'),
|
||||
('Optimization Methods and Hyperparameters',
|
||||
2,
|
||||
None,
|
||||
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|
||||
('Resampling', 2, None, 'resampling'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
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|
||||
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|
||||
('Additional courses of interest',
|
||||
2,
|
||||
None,
|
||||
'additional-courses-of-interest'),
|
||||
("What's the future like?", 2, None, 'what-s-the-future-like'),
|
||||
('Types of Machine Learning, a repetition',
|
||||
2,
|
||||
None,
|
||||
'types-of-machine-learning-a-repetition'),
|
||||
('Why Boltzmann machines?', 2, None, 'why-boltzmann-machines'),
|
||||
('Boltzmann Machines', 2, None, 'boltzmann-machines'),
|
||||
('Some similarities and differences from DNNs',
|
||||
2,
|
||||
None,
|
||||
'some-similarities-and-differences-from-dnns'),
|
||||
('Boltzmann machines (BM)', 2, None, 'boltzmann-machines-bm'),
|
||||
('A standard BM setup', 2, None, 'a-standard-bm-setup'),
|
||||
('The structure of the RBM network',
|
||||
2,
|
||||
None,
|
||||
'the-structure-of-the-rbm-network'),
|
||||
('The network', 2, None, 'the-network'),
|
||||
('Goals', 2, None, 'goals'),
|
||||
('Joint distribution', 2, None, 'joint-distribution'),
|
||||
('Network Elements, the energy function',
|
||||
2,
|
||||
None,
|
||||
'network-elements-the-energy-function'),
|
||||
('Defining different types of RBMs',
|
||||
2,
|
||||
None,
|
||||
'defining-different-types-of-rbms'),
|
||||
('More about RBMs', 2, None, 'more-about-rbms'),
|
||||
('Autoencoders: Overarching view',
|
||||
2,
|
||||
None,
|
||||
'autoencoders-overarching-view'),
|
||||
('Bayesian Machine Learning',
|
||||
2,
|
||||
None,
|
||||
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|
||||
('Reinforcement Learning', 2, None, 'reinforcement-learning'),
|
||||
('Transfer learning', 2, None, 'transfer-learning'),
|
||||
('Adversarial learning', 2, None, 'adversarial-learning'),
|
||||
('Dual learning', 2, None, 'dual-learning'),
|
||||
('Distributed machine learning',
|
||||
2,
|
||||
None,
|
||||
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|
||||
('Meta learning', 2, None, 'meta-learning'),
|
||||
('The Challenges Facing Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'the-challenges-facing-machine-learning'),
|
||||
('Explainable machine learning',
|
||||
2,
|
||||
None,
|
||||
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|
||||
('Scientific Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'scientific-machine-learning'),
|
||||
('Quantum machine learning', 2, None, 'quantum-machine-learning'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'quantum-machine-learning-algorithms-based-on-linear-algebra'),
|
||||
('Quantum reinforcement learning',
|
||||
2,
|
||||
None,
|
||||
'quantum-reinforcement-learning'),
|
||||
('Quantum deep learning', 2, None, 'quantum-deep-learning'),
|
||||
('Social machine learning', 2, None, 'social-machine-learning'),
|
||||
('The last words?', 2, None, 'the-last-words'),
|
||||
('AI/ML and some statements you may have heard (and what do they '
|
||||
'mean?)',
|
||||
2,
|
||||
None,
|
||||
'ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester')]}
|
||||
end of tocinfo -->
|
||||
|
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<a class="navbar-brand" href="week47-bs.html">Week 47: Unsupervised learning (PCA and Clustering) 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="._week47-bs001.html#overview-of-week-47" style="font-size: 80%;"><b>Overview of week 47</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs002.html#basic-ideas-of-the-principal-component-analysis-pca" style="font-size: 80%;"><b>Basic ideas of the Principal Component Analysis (PCA)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs003.html#introducing-the-covariance-and-correlation-functions" style="font-size: 80%;"><b>Introducing the Covariance and Correlation functions</b></a></li>
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||||
<!-- navigation toc: --> <li><a href="._week47-bs004.html#more-on-the-covariance" style="font-size: 80%;"><b>More on the covariance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs005.html#reminding-ourselves-about-linear-regression" style="font-size: 80%;"><b>Reminding ourselves about Linear Regression</b></a></li>
|
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<!-- navigation toc: --> <li><a href="._week47-bs006.html#simple-example" style="font-size: 80%;"><b>Simple Example</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs007.html#the-correlation-matrix" style="font-size: 80%;"><b>The Correlation Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs008.html#numpy-functionality" style="font-size: 80%;"><b>Numpy Functionality</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs009.html#correlation-matrix-again" style="font-size: 80%;"><b>Correlation Matrix again</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs010.html#using-pandas" style="font-size: 80%;"><b>Using Pandas</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs011.html#and-then-the-franke-function" style="font-size: 80%;"><b>And then the Franke Function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs012.html#links-with-the-design-matrix" style="font-size: 80%;"><b>Links with the Design Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs013.html#computing-the-expectation-values" style="font-size: 80%;"><b>Computing the Expectation Values</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs014.html#towards-the-pca-theorem" style="font-size: 80%;"><b>Towards the PCA theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs015.html#more-on-the-pca-theorem" style="font-size: 80%;"><b>More on the PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs016.html#a-kind-of-bird-s-view-on-pca" style="font-size: 80%;"><b>A kind of Bird's view on PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs017.html#writing-our-own-pca-code" style="font-size: 80%;"><b>Writing our own PCA code</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs018.html#implementing-it" style="font-size: 80%;"><b>Implementing it</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs019.html#first-step" style="font-size: 80%;"><b>First Step</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs020.html#scaling" style="font-size: 80%;"><b>Scaling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs021.html#centered-data" style="font-size: 80%;"><b>Centered Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs022.html#exploring" style="font-size: 80%;"><b>Exploring</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs023.html#diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components" style="font-size: 80%;"><b>Diagonalize the sample covariance matrix to obtain the principal components</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs024.html#collecting-all-steps" style="font-size: 80%;"><b>Collecting all Steps</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs025.html#classical-pca-theorem" style="font-size: 80%;"><b>Classical PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs026.html#the-pca-theorem" style="font-size: 80%;"><b>The PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs027.html#geometric-interpretation-and-link-with-singular-value-decomposition" style="font-size: 80%;"><b>Geometric Interpretation and link with Singular Value Decomposition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs028.html#pca-and-scikit-learn" style="font-size: 80%;"><b>PCA and scikit-learn</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs029.html#back-to-the-cancer-data" style="font-size: 80%;"><b>Back to the Cancer Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#incremental-pca" style="font-size: 80%;"><b>Incremental PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#randomized-pca" style="font-size: 80%;"> Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#kernel-pca" style="font-size: 80%;"> Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs031.html#other-techniques" style="font-size: 80%;"><b>Other techniques</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs032.html#clustering-and-unsupervised-learning" style="font-size: 80%;"><b>Clustering and Unsupervised Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs033.html#basic-idea-of-the-k-means-clustering-algorithm" style="font-size: 80%;"><b>Basic Idea of the \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs034.html#the-k-means-algorithm" style="font-size: 80%;"><b>The \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs035.html#basic-math-of-the-k-means-algorithm" style="font-size: 80%;"><b>Basic Math of the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs036.html#within-cluster-point-scatter" style="font-size: 80%;"><b>Within Cluster Point Scatter</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs037.html#more-details" style="font-size: 80%;"><b>More Details</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs038.html#total-cluster-variance" style="font-size: 80%;"><b>Total Cluster Variance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs039.html#the-k-means-clustering-algorithm" style="font-size: 80%;"><b>The \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs040.html#summarizing" style="font-size: 80%;"><b>Summarizing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs041.html#writing-our-own-code-the-data-set" style="font-size: 80%;"><b>Writing our own Code, the Data Set</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs042.html#implementing-the-k-means-algorithm" style="font-size: 80%;"><b>Implementing the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs043.html#plotting" style="font-size: 80%;"><b>Plotting</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs044.html#continuing" style="font-size: 80%;"><b>Continuing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs045.html#wrapping-it-up" style="font-size: 80%;"><b>Wrapping it up</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs046.html#summary-of-course" style="font-size: 80%;"><b>Summary of course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs047.html#what-me-worry-no-final-exam-in-this-course" style="font-size: 80%;"><b>What? Me worry? No final exam in this course!</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs048.html#what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks" style="font-size: 80%;"><b>What is the link between Artificial Intelligence and Machine Learning and some general Remarks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs049.html#going-back-to-the-beginning-of-the-semester" style="font-size: 80%;"><b>Going back to the beginning of the semester</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs050.html#not-so-sharp-distinctions" style="font-size: 80%;"><b>Not so sharp distinctions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs051.html#topics-we-have-covered-this-year" style="font-size: 80%;"><b>Topics we have covered this year</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs052.html#statistical-analysis-and-optimization-of-data" style="font-size: 80%;"><b>Statistical analysis and optimization of data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs053.html#machine-learning" style="font-size: 80%;"><b>Machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs054.html#learning-outcomes-and-overarching-aims-of-this-course" style="font-size: 80%;"><b>Learning outcomes and overarching aims of this course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs055.html#perspective-on-machine-learning" style="font-size: 80%;"><b>Perspective on Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs056.html#machine-learning-research" style="font-size: 80%;"><b>Machine Learning Research</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs057.html#starting-your-machine-learning-project" style="font-size: 80%;"><b>Starting your Machine Learning Project</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs058.html#choose-a-model-and-algorithm" style="font-size: 80%;"><b>Choose a Model and Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs059.html#preparing-your-data" style="font-size: 80%;"><b>Preparing Your Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs060.html#which-activation-and-weights-to-choose-in-neural-networks" style="font-size: 80%;"><b>Which Activation and Weights to Choose in Neural Networks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs061.html#optimization-methods-and-hyperparameters" style="font-size: 80%;"><b>Optimization Methods and Hyperparameters</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs062.html#resampling" style="font-size: 80%;"><b>Resampling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs063.html#other-courses-on-data-science-and-machine-learning-at-uio" style="font-size: 80%;"><b>Other courses on Data science and Machine Learning at UiO</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs064.html#additional-courses-of-interest" style="font-size: 80%;"><b>Additional courses of interest</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs065.html#what-s-the-future-like" style="font-size: 80%;"><b>What's the future like?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs066.html#types-of-machine-learning-a-repetition" style="font-size: 80%;"><b>Types of Machine Learning, a repetition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs067.html#why-boltzmann-machines" style="font-size: 80%;"><b>Why Boltzmann machines?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs068.html#boltzmann-machines" style="font-size: 80%;"><b>Boltzmann Machines</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs069.html#some-similarities-and-differences-from-dnns" style="font-size: 80%;"><b>Some similarities and differences from DNNs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs070.html#boltzmann-machines-bm" style="font-size: 80%;"><b>Boltzmann machines (BM)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs071.html#a-standard-bm-setup" style="font-size: 80%;"><b>A standard BM setup</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs072.html#the-structure-of-the-rbm-network" style="font-size: 80%;"><b>The structure of the RBM network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs073.html#the-network" style="font-size: 80%;"><b>The network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs074.html#goals" style="font-size: 80%;"><b>Goals</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs075.html#joint-distribution" style="font-size: 80%;"><b>Joint distribution</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs076.html#network-elements-the-energy-function" style="font-size: 80%;"><b>Network Elements, the energy function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs077.html#defining-different-types-of-rbms" style="font-size: 80%;"><b>Defining different types of RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs078.html#more-about-rbms" style="font-size: 80%;"><b>More about RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs079.html#autoencoders-overarching-view" style="font-size: 80%;"><b>Autoencoders: Overarching view</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs080.html#bayesian-machine-learning" style="font-size: 80%;"><b>Bayesian Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs081.html#reinforcement-learning" style="font-size: 80%;"><b>Reinforcement Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs082.html#transfer-learning" style="font-size: 80%;"><b>Transfer learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs083.html#adversarial-learning" style="font-size: 80%;"><b>Adversarial learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs084.html#dual-learning" style="font-size: 80%;"><b>Dual learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs085.html#distributed-machine-learning" style="font-size: 80%;"><b>Distributed machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs086.html#meta-learning" style="font-size: 80%;"><b>Meta learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs087.html#the-challenges-facing-machine-learning" style="font-size: 80%;"><b>The Challenges Facing Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs088.html#explainable-machine-learning" style="font-size: 80%;"><b>Explainable machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs089.html#scientific-machine-learning" style="font-size: 80%;"><b>Scientific Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs090.html#quantum-machine-learning" style="font-size: 80%;"><b>Quantum machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs091.html#quantum-machine-learning-algorithms-based-on-linear-algebra" style="font-size: 80%;"><b>Quantum machine learning algorithms based on linear algebra</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="#quantum-reinforcement-learning" style="font-size: 80%;"><b>Quantum reinforcement learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs093.html#quantum-deep-learning" style="font-size: 80%;"><b>Quantum deep learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs094.html#social-machine-learning" style="font-size: 80%;"><b>Social machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs095.html#the-last-words" style="font-size: 80%;"><b>The last words?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs096.html#ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean" style="font-size: 80%;"><b>AI/ML and some statements you may have heard (and what do they mean?)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs097.html#best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester" style="font-size: 80%;"><b>Best wishes to you all and thanks so much for your heroic efforts this semester</b></a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div> <!-- end of navigation bar -->
|
||||
<div class="container">
|
||||
<p> </p><p> </p><p> </p> <!-- add vertical space -->
|
||||
<a name="part0092"></a>
|
||||
<!-- !split -->
|
||||
<h2 id="quantum-reinforcement-learning" 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
|
||||
adjust and improve its behavioral strategies. In some cases, it
|
||||
achieves quantum acceleration by the quantum processing capabilities
|
||||
of the agent or the possibility of exploring the environment through
|
||||
quantum superposition. Such algorithms have been proposed in
|
||||
superconducting circuits and systems of trapped ions.
|
||||
</p>
|
||||
|
||||
<p>
|
||||
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|
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<li><a href="._week47-bs097.html">98</a></li>
|
||||
<li><a href="._week47-bs093.html">»</a></li>
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|
||||
('Basic ideas of the Principal Component Analysis (PCA)',
|
||||
2,
|
||||
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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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|
||||
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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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|
||||
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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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|
||||
2,
|
||||
None,
|
||||
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|
||||
('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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<a class="navbar-brand" href="week47-bs.html">Week 47: Unsupervised learning (PCA and Clustering) and Summary of Course</a>
|
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|
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|
||||
<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
|
||||
<ul class="dropdown-menu">
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs001.html#overview-of-week-47" style="font-size: 80%;"><b>Overview of week 47</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs002.html#basic-ideas-of-the-principal-component-analysis-pca" style="font-size: 80%;"><b>Basic ideas of the Principal Component Analysis (PCA)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs003.html#introducing-the-covariance-and-correlation-functions" style="font-size: 80%;"><b>Introducing the Covariance and Correlation functions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs004.html#more-on-the-covariance" style="font-size: 80%;"><b>More on the covariance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs005.html#reminding-ourselves-about-linear-regression" style="font-size: 80%;"><b>Reminding ourselves about Linear Regression</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs006.html#simple-example" style="font-size: 80%;"><b>Simple Example</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs007.html#the-correlation-matrix" style="font-size: 80%;"><b>The Correlation Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs008.html#numpy-functionality" style="font-size: 80%;"><b>Numpy Functionality</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs009.html#correlation-matrix-again" style="font-size: 80%;"><b>Correlation Matrix again</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs010.html#using-pandas" style="font-size: 80%;"><b>Using Pandas</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs011.html#and-then-the-franke-function" style="font-size: 80%;"><b>And then the Franke Function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs012.html#links-with-the-design-matrix" style="font-size: 80%;"><b>Links with the Design Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs013.html#computing-the-expectation-values" style="font-size: 80%;"><b>Computing the Expectation Values</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs014.html#towards-the-pca-theorem" style="font-size: 80%;"><b>Towards the PCA theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs015.html#more-on-the-pca-theorem" style="font-size: 80%;"><b>More on the PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs016.html#a-kind-of-bird-s-view-on-pca" style="font-size: 80%;"><b>A kind of Bird's view on PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs017.html#writing-our-own-pca-code" style="font-size: 80%;"><b>Writing our own PCA code</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs018.html#implementing-it" style="font-size: 80%;"><b>Implementing it</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs019.html#first-step" style="font-size: 80%;"><b>First Step</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs020.html#scaling" style="font-size: 80%;"><b>Scaling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs021.html#centered-data" style="font-size: 80%;"><b>Centered Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs022.html#exploring" style="font-size: 80%;"><b>Exploring</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs023.html#diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components" style="font-size: 80%;"><b>Diagonalize the sample covariance matrix to obtain the principal components</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs024.html#collecting-all-steps" style="font-size: 80%;"><b>Collecting all Steps</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs025.html#classical-pca-theorem" style="font-size: 80%;"><b>Classical PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs026.html#the-pca-theorem" style="font-size: 80%;"><b>The PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs027.html#geometric-interpretation-and-link-with-singular-value-decomposition" style="font-size: 80%;"><b>Geometric Interpretation and link with Singular Value Decomposition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs028.html#pca-and-scikit-learn" style="font-size: 80%;"><b>PCA and scikit-learn</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs029.html#back-to-the-cancer-data" style="font-size: 80%;"><b>Back to the Cancer Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#incremental-pca" style="font-size: 80%;"><b>Incremental PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#randomized-pca" style="font-size: 80%;"> Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#kernel-pca" style="font-size: 80%;"> Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs031.html#other-techniques" style="font-size: 80%;"><b>Other techniques</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs032.html#clustering-and-unsupervised-learning" style="font-size: 80%;"><b>Clustering and Unsupervised Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs033.html#basic-idea-of-the-k-means-clustering-algorithm" style="font-size: 80%;"><b>Basic Idea of the \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs034.html#the-k-means-algorithm" style="font-size: 80%;"><b>The \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs035.html#basic-math-of-the-k-means-algorithm" style="font-size: 80%;"><b>Basic Math of the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs036.html#within-cluster-point-scatter" style="font-size: 80%;"><b>Within Cluster Point Scatter</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs037.html#more-details" style="font-size: 80%;"><b>More Details</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs038.html#total-cluster-variance" style="font-size: 80%;"><b>Total Cluster Variance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs039.html#the-k-means-clustering-algorithm" style="font-size: 80%;"><b>The \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs040.html#summarizing" style="font-size: 80%;"><b>Summarizing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs041.html#writing-our-own-code-the-data-set" style="font-size: 80%;"><b>Writing our own Code, the Data Set</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs042.html#implementing-the-k-means-algorithm" style="font-size: 80%;"><b>Implementing the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs043.html#plotting" style="font-size: 80%;"><b>Plotting</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs044.html#continuing" style="font-size: 80%;"><b>Continuing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs045.html#wrapping-it-up" style="font-size: 80%;"><b>Wrapping it up</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs046.html#summary-of-course" style="font-size: 80%;"><b>Summary of course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs047.html#what-me-worry-no-final-exam-in-this-course" style="font-size: 80%;"><b>What? Me worry? No final exam in this course!</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs048.html#what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks" style="font-size: 80%;"><b>What is the link between Artificial Intelligence and Machine Learning and some general Remarks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs049.html#going-back-to-the-beginning-of-the-semester" style="font-size: 80%;"><b>Going back to the beginning of the semester</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs050.html#not-so-sharp-distinctions" style="font-size: 80%;"><b>Not so sharp distinctions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs051.html#topics-we-have-covered-this-year" style="font-size: 80%;"><b>Topics we have covered this year</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs052.html#statistical-analysis-and-optimization-of-data" style="font-size: 80%;"><b>Statistical analysis and optimization of data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs053.html#machine-learning" style="font-size: 80%;"><b>Machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs054.html#learning-outcomes-and-overarching-aims-of-this-course" style="font-size: 80%;"><b>Learning outcomes and overarching aims of this course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs055.html#perspective-on-machine-learning" style="font-size: 80%;"><b>Perspective on Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs056.html#machine-learning-research" style="font-size: 80%;"><b>Machine Learning Research</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs057.html#starting-your-machine-learning-project" style="font-size: 80%;"><b>Starting your Machine Learning Project</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs058.html#choose-a-model-and-algorithm" style="font-size: 80%;"><b>Choose a Model and Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs059.html#preparing-your-data" style="font-size: 80%;"><b>Preparing Your Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs060.html#which-activation-and-weights-to-choose-in-neural-networks" style="font-size: 80%;"><b>Which Activation and Weights to Choose in Neural Networks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs061.html#optimization-methods-and-hyperparameters" style="font-size: 80%;"><b>Optimization Methods and Hyperparameters</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs062.html#resampling" style="font-size: 80%;"><b>Resampling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs063.html#other-courses-on-data-science-and-machine-learning-at-uio" style="font-size: 80%;"><b>Other courses on Data science and Machine Learning at UiO</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs064.html#additional-courses-of-interest" style="font-size: 80%;"><b>Additional courses of interest</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs065.html#what-s-the-future-like" style="font-size: 80%;"><b>What's the future like?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs066.html#types-of-machine-learning-a-repetition" style="font-size: 80%;"><b>Types of Machine Learning, a repetition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs067.html#why-boltzmann-machines" style="font-size: 80%;"><b>Why Boltzmann machines?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs068.html#boltzmann-machines" style="font-size: 80%;"><b>Boltzmann Machines</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs069.html#some-similarities-and-differences-from-dnns" style="font-size: 80%;"><b>Some similarities and differences from DNNs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs070.html#boltzmann-machines-bm" style="font-size: 80%;"><b>Boltzmann machines (BM)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs071.html#a-standard-bm-setup" style="font-size: 80%;"><b>A standard BM setup</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs072.html#the-structure-of-the-rbm-network" style="font-size: 80%;"><b>The structure of the RBM network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs073.html#the-network" style="font-size: 80%;"><b>The network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs074.html#goals" style="font-size: 80%;"><b>Goals</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs075.html#joint-distribution" style="font-size: 80%;"><b>Joint distribution</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs076.html#network-elements-the-energy-function" style="font-size: 80%;"><b>Network Elements, the energy function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs077.html#defining-different-types-of-rbms" style="font-size: 80%;"><b>Defining different types of RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs078.html#more-about-rbms" style="font-size: 80%;"><b>More about RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs079.html#autoencoders-overarching-view" style="font-size: 80%;"><b>Autoencoders: Overarching view</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs080.html#bayesian-machine-learning" style="font-size: 80%;"><b>Bayesian Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs081.html#reinforcement-learning" style="font-size: 80%;"><b>Reinforcement Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs082.html#transfer-learning" style="font-size: 80%;"><b>Transfer learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs083.html#adversarial-learning" style="font-size: 80%;"><b>Adversarial learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs084.html#dual-learning" style="font-size: 80%;"><b>Dual learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs085.html#distributed-machine-learning" style="font-size: 80%;"><b>Distributed machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs086.html#meta-learning" style="font-size: 80%;"><b>Meta learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs087.html#the-challenges-facing-machine-learning" style="font-size: 80%;"><b>The Challenges Facing Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs088.html#explainable-machine-learning" style="font-size: 80%;"><b>Explainable machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs089.html#scientific-machine-learning" style="font-size: 80%;"><b>Scientific Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs090.html#quantum-machine-learning" style="font-size: 80%;"><b>Quantum machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs091.html#quantum-machine-learning-algorithms-based-on-linear-algebra" style="font-size: 80%;"><b>Quantum machine learning algorithms based on linear algebra</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs092.html#quantum-reinforcement-learning" style="font-size: 80%;"><b>Quantum reinforcement learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="#quantum-deep-learning" style="font-size: 80%;"><b>Quantum deep learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs094.html#social-machine-learning" style="font-size: 80%;"><b>Social machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs095.html#the-last-words" style="font-size: 80%;"><b>The last words?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs096.html#ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean" style="font-size: 80%;"><b>AI/ML and some statements you may have heard (and what do they mean?)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs097.html#best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester" style="font-size: 80%;"><b>Best wishes to you all and thanks so much for your heroic efforts this semester</b></a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
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|
||||
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|
||||
</div> <!-- end of navigation bar -->
|
||||
<div class="container">
|
||||
<p> </p><p> </p><p> </p> <!-- add vertical space -->
|
||||
<a name="part0093"></a>
|
||||
<!-- !split -->
|
||||
<h2 id="quantum-deep-learning" class="anchor">Quantum deep learning </h2>
|
||||
|
||||
<p>Dedicated quantum information processors, such as quantum annealers
|
||||
and programmable photonic circuits, are well suited for building deep
|
||||
quantum networks. The simplest deep quantum network is the Boltzmann
|
||||
machine. The classical Boltzmann machine consists of bits with tunable
|
||||
interactions and is trained by adjusting the interaction of these bits
|
||||
so that the distribution of its expression conforms to the statistics
|
||||
of the data. To quantize the Boltzmann machine, the neural network can
|
||||
simply be represented as a set of interacting quantum spins that
|
||||
correspond to an adjustable Ising model. Then, by initializing the
|
||||
input neurons in the Boltzmann machine to a fixed state and allowing
|
||||
the system to heat up, we can read out the output qubits to get the
|
||||
result.
|
||||
</p>
|
||||
|
||||
<p>
|
||||
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|
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('Basic ideas of the Principal Component Analysis (PCA)',
|
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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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||||
('Diagonalize the sample covariance matrix to obtain 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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|
||||
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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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|
||||
'implementing-the-k-means-algorithm'),
|
||||
('Plotting', 2, None, 'plotting'),
|
||||
('Continuing', 2, None, 'continuing'),
|
||||
('Wrapping it up', 2, None, 'wrapping-it-up'),
|
||||
('Summary of course', 2, None, 'summary-of-course'),
|
||||
('What? Me worry? No final exam in this course!',
|
||||
2,
|
||||
None,
|
||||
'what-me-worry-no-final-exam-in-this-course'),
|
||||
('What is the link between Artificial Intelligence and Machine '
|
||||
'Learning and some general Remarks',
|
||||
2,
|
||||
None,
|
||||
'what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks'),
|
||||
('Going back to the beginning of the semester',
|
||||
2,
|
||||
None,
|
||||
'going-back-to-the-beginning-of-the-semester'),
|
||||
('Not so sharp distinctions',
|
||||
2,
|
||||
None,
|
||||
'not-so-sharp-distinctions'),
|
||||
('Topics we have covered this year',
|
||||
2,
|
||||
None,
|
||||
'topics-we-have-covered-this-year'),
|
||||
('Statistical analysis and optimization of data',
|
||||
2,
|
||||
None,
|
||||
'statistical-analysis-and-optimization-of-data'),
|
||||
('Machine learning', 2, None, 'machine-learning'),
|
||||
('Learning outcomes and overarching aims of this course',
|
||||
2,
|
||||
None,
|
||||
'learning-outcomes-and-overarching-aims-of-this-course'),
|
||||
('Perspective on Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'perspective-on-machine-learning'),
|
||||
('Machine Learning Research',
|
||||
2,
|
||||
None,
|
||||
'machine-learning-research'),
|
||||
('Starting your Machine Learning Project',
|
||||
2,
|
||||
None,
|
||||
'starting-your-machine-learning-project'),
|
||||
('Choose a Model and Algorithm',
|
||||
2,
|
||||
None,
|
||||
'choose-a-model-and-algorithm'),
|
||||
('Preparing Your Data', 2, None, 'preparing-your-data'),
|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
||||
2,
|
||||
None,
|
||||
'which-activation-and-weights-to-choose-in-neural-networks'),
|
||||
('Optimization Methods and Hyperparameters',
|
||||
2,
|
||||
None,
|
||||
'optimization-methods-and-hyperparameters'),
|
||||
('Resampling', 2, None, 'resampling'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
||||
'other-courses-on-data-science-and-machine-learning-at-uio'),
|
||||
('Additional courses of interest',
|
||||
2,
|
||||
None,
|
||||
'additional-courses-of-interest'),
|
||||
("What's the future like?", 2, None, 'what-s-the-future-like'),
|
||||
('Types of Machine Learning, a repetition',
|
||||
2,
|
||||
None,
|
||||
'types-of-machine-learning-a-repetition'),
|
||||
('Why Boltzmann machines?', 2, None, 'why-boltzmann-machines'),
|
||||
('Boltzmann Machines', 2, None, 'boltzmann-machines'),
|
||||
('Some similarities and differences from DNNs',
|
||||
2,
|
||||
None,
|
||||
'some-similarities-and-differences-from-dnns'),
|
||||
('Boltzmann machines (BM)', 2, None, 'boltzmann-machines-bm'),
|
||||
('A standard BM setup', 2, None, 'a-standard-bm-setup'),
|
||||
('The structure of the RBM network',
|
||||
2,
|
||||
None,
|
||||
'the-structure-of-the-rbm-network'),
|
||||
('The network', 2, None, 'the-network'),
|
||||
('Goals', 2, None, 'goals'),
|
||||
('Joint distribution', 2, None, 'joint-distribution'),
|
||||
('Network Elements, the energy function',
|
||||
2,
|
||||
None,
|
||||
'network-elements-the-energy-function'),
|
||||
('Defining different types of RBMs',
|
||||
2,
|
||||
None,
|
||||
'defining-different-types-of-rbms'),
|
||||
('More about RBMs', 2, None, 'more-about-rbms'),
|
||||
('Autoencoders: Overarching view',
|
||||
2,
|
||||
None,
|
||||
'autoencoders-overarching-view'),
|
||||
('Bayesian Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'bayesian-machine-learning'),
|
||||
('Reinforcement Learning', 2, None, 'reinforcement-learning'),
|
||||
('Transfer learning', 2, None, 'transfer-learning'),
|
||||
('Adversarial learning', 2, None, 'adversarial-learning'),
|
||||
('Dual learning', 2, None, 'dual-learning'),
|
||||
('Distributed machine learning',
|
||||
2,
|
||||
None,
|
||||
'distributed-machine-learning'),
|
||||
('Meta learning', 2, None, 'meta-learning'),
|
||||
('The Challenges Facing Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'the-challenges-facing-machine-learning'),
|
||||
('Explainable machine learning',
|
||||
2,
|
||||
None,
|
||||
'explainable-machine-learning'),
|
||||
('Scientific Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'scientific-machine-learning'),
|
||||
('Quantum machine learning', 2, None, 'quantum-machine-learning'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'quantum-machine-learning-algorithms-based-on-linear-algebra'),
|
||||
('Quantum reinforcement learning',
|
||||
2,
|
||||
None,
|
||||
'quantum-reinforcement-learning'),
|
||||
('Quantum deep learning', 2, None, 'quantum-deep-learning'),
|
||||
('Social machine learning', 2, None, 'social-machine-learning'),
|
||||
('The last words?', 2, None, 'the-last-words'),
|
||||
('AI/ML and some statements you may have heard (and what do they '
|
||||
'mean?)',
|
||||
2,
|
||||
None,
|
||||
'ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester')]}
|
||||
end of tocinfo -->
|
||||
|
||||
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|
||||
|
||||
|
||||
|
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|
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<a class="navbar-brand" href="week47-bs.html">Week 47: Unsupervised learning (PCA and Clustering) and Summary of Course</a>
|
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|
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|
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<ul class="nav navbar-nav navbar-right">
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<li class="dropdown">
|
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<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
|
||||
<ul class="dropdown-menu">
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs001.html#overview-of-week-47" style="font-size: 80%;"><b>Overview of week 47</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs002.html#basic-ideas-of-the-principal-component-analysis-pca" style="font-size: 80%;"><b>Basic ideas of the Principal Component Analysis (PCA)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs003.html#introducing-the-covariance-and-correlation-functions" style="font-size: 80%;"><b>Introducing the Covariance and Correlation functions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs004.html#more-on-the-covariance" style="font-size: 80%;"><b>More on the covariance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs005.html#reminding-ourselves-about-linear-regression" style="font-size: 80%;"><b>Reminding ourselves about Linear Regression</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs006.html#simple-example" style="font-size: 80%;"><b>Simple Example</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs007.html#the-correlation-matrix" style="font-size: 80%;"><b>The Correlation Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs008.html#numpy-functionality" style="font-size: 80%;"><b>Numpy Functionality</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs009.html#correlation-matrix-again" style="font-size: 80%;"><b>Correlation Matrix again</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs010.html#using-pandas" style="font-size: 80%;"><b>Using Pandas</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs011.html#and-then-the-franke-function" style="font-size: 80%;"><b>And then the Franke Function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs012.html#links-with-the-design-matrix" style="font-size: 80%;"><b>Links with the Design Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs013.html#computing-the-expectation-values" style="font-size: 80%;"><b>Computing the Expectation Values</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs014.html#towards-the-pca-theorem" style="font-size: 80%;"><b>Towards the PCA theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs015.html#more-on-the-pca-theorem" style="font-size: 80%;"><b>More on the PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs016.html#a-kind-of-bird-s-view-on-pca" style="font-size: 80%;"><b>A kind of Bird's view on PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs017.html#writing-our-own-pca-code" style="font-size: 80%;"><b>Writing our own PCA code</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs018.html#implementing-it" style="font-size: 80%;"><b>Implementing it</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs019.html#first-step" style="font-size: 80%;"><b>First Step</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs020.html#scaling" style="font-size: 80%;"><b>Scaling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs021.html#centered-data" style="font-size: 80%;"><b>Centered Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs022.html#exploring" style="font-size: 80%;"><b>Exploring</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs023.html#diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components" style="font-size: 80%;"><b>Diagonalize the sample covariance matrix to obtain the principal components</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs024.html#collecting-all-steps" style="font-size: 80%;"><b>Collecting all Steps</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs025.html#classical-pca-theorem" style="font-size: 80%;"><b>Classical PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs026.html#the-pca-theorem" style="font-size: 80%;"><b>The PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs027.html#geometric-interpretation-and-link-with-singular-value-decomposition" style="font-size: 80%;"><b>Geometric Interpretation and link with Singular Value Decomposition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs028.html#pca-and-scikit-learn" style="font-size: 80%;"><b>PCA and scikit-learn</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs029.html#back-to-the-cancer-data" style="font-size: 80%;"><b>Back to the Cancer Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#incremental-pca" style="font-size: 80%;"><b>Incremental PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#randomized-pca" style="font-size: 80%;"> Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#kernel-pca" style="font-size: 80%;"> Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs031.html#other-techniques" style="font-size: 80%;"><b>Other techniques</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs032.html#clustering-and-unsupervised-learning" style="font-size: 80%;"><b>Clustering and Unsupervised Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs033.html#basic-idea-of-the-k-means-clustering-algorithm" style="font-size: 80%;"><b>Basic Idea of the \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs034.html#the-k-means-algorithm" style="font-size: 80%;"><b>The \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs035.html#basic-math-of-the-k-means-algorithm" style="font-size: 80%;"><b>Basic Math of the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs036.html#within-cluster-point-scatter" style="font-size: 80%;"><b>Within Cluster Point Scatter</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs037.html#more-details" style="font-size: 80%;"><b>More Details</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs038.html#total-cluster-variance" style="font-size: 80%;"><b>Total Cluster Variance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs039.html#the-k-means-clustering-algorithm" style="font-size: 80%;"><b>The \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs040.html#summarizing" style="font-size: 80%;"><b>Summarizing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs041.html#writing-our-own-code-the-data-set" style="font-size: 80%;"><b>Writing our own Code, the Data Set</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs042.html#implementing-the-k-means-algorithm" style="font-size: 80%;"><b>Implementing the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs043.html#plotting" style="font-size: 80%;"><b>Plotting</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs044.html#continuing" style="font-size: 80%;"><b>Continuing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs045.html#wrapping-it-up" style="font-size: 80%;"><b>Wrapping it up</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs046.html#summary-of-course" style="font-size: 80%;"><b>Summary of course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs047.html#what-me-worry-no-final-exam-in-this-course" style="font-size: 80%;"><b>What? Me worry? No final exam in this course!</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs048.html#what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks" style="font-size: 80%;"><b>What is the link between Artificial Intelligence and Machine Learning and some general Remarks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs049.html#going-back-to-the-beginning-of-the-semester" style="font-size: 80%;"><b>Going back to the beginning of the semester</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs050.html#not-so-sharp-distinctions" style="font-size: 80%;"><b>Not so sharp distinctions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs051.html#topics-we-have-covered-this-year" style="font-size: 80%;"><b>Topics we have covered this year</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs052.html#statistical-analysis-and-optimization-of-data" style="font-size: 80%;"><b>Statistical analysis and optimization of data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs053.html#machine-learning" style="font-size: 80%;"><b>Machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs054.html#learning-outcomes-and-overarching-aims-of-this-course" style="font-size: 80%;"><b>Learning outcomes and overarching aims of this course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs055.html#perspective-on-machine-learning" style="font-size: 80%;"><b>Perspective on Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs056.html#machine-learning-research" style="font-size: 80%;"><b>Machine Learning Research</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs057.html#starting-your-machine-learning-project" style="font-size: 80%;"><b>Starting your Machine Learning Project</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs058.html#choose-a-model-and-algorithm" style="font-size: 80%;"><b>Choose a Model and Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs059.html#preparing-your-data" style="font-size: 80%;"><b>Preparing Your Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs060.html#which-activation-and-weights-to-choose-in-neural-networks" style="font-size: 80%;"><b>Which Activation and Weights to Choose in Neural Networks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs061.html#optimization-methods-and-hyperparameters" style="font-size: 80%;"><b>Optimization Methods and Hyperparameters</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs062.html#resampling" style="font-size: 80%;"><b>Resampling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs063.html#other-courses-on-data-science-and-machine-learning-at-uio" style="font-size: 80%;"><b>Other courses on Data science and Machine Learning at UiO</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs064.html#additional-courses-of-interest" style="font-size: 80%;"><b>Additional courses of interest</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs065.html#what-s-the-future-like" style="font-size: 80%;"><b>What's the future like?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs066.html#types-of-machine-learning-a-repetition" style="font-size: 80%;"><b>Types of Machine Learning, a repetition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs067.html#why-boltzmann-machines" style="font-size: 80%;"><b>Why Boltzmann machines?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs068.html#boltzmann-machines" style="font-size: 80%;"><b>Boltzmann Machines</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs069.html#some-similarities-and-differences-from-dnns" style="font-size: 80%;"><b>Some similarities and differences from DNNs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs070.html#boltzmann-machines-bm" style="font-size: 80%;"><b>Boltzmann machines (BM)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs071.html#a-standard-bm-setup" style="font-size: 80%;"><b>A standard BM setup</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs072.html#the-structure-of-the-rbm-network" style="font-size: 80%;"><b>The structure of the RBM network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs073.html#the-network" style="font-size: 80%;"><b>The network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs074.html#goals" style="font-size: 80%;"><b>Goals</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs075.html#joint-distribution" style="font-size: 80%;"><b>Joint distribution</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs076.html#network-elements-the-energy-function" style="font-size: 80%;"><b>Network Elements, the energy function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs077.html#defining-different-types-of-rbms" style="font-size: 80%;"><b>Defining different types of RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs078.html#more-about-rbms" style="font-size: 80%;"><b>More about RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs079.html#autoencoders-overarching-view" style="font-size: 80%;"><b>Autoencoders: Overarching view</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs080.html#bayesian-machine-learning" style="font-size: 80%;"><b>Bayesian Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs081.html#reinforcement-learning" style="font-size: 80%;"><b>Reinforcement Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs082.html#transfer-learning" style="font-size: 80%;"><b>Transfer learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs083.html#adversarial-learning" style="font-size: 80%;"><b>Adversarial learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs084.html#dual-learning" style="font-size: 80%;"><b>Dual learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs085.html#distributed-machine-learning" style="font-size: 80%;"><b>Distributed machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs086.html#meta-learning" style="font-size: 80%;"><b>Meta learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs087.html#the-challenges-facing-machine-learning" style="font-size: 80%;"><b>The Challenges Facing Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs088.html#explainable-machine-learning" style="font-size: 80%;"><b>Explainable machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs089.html#scientific-machine-learning" style="font-size: 80%;"><b>Scientific Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs090.html#quantum-machine-learning" style="font-size: 80%;"><b>Quantum machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs091.html#quantum-machine-learning-algorithms-based-on-linear-algebra" style="font-size: 80%;"><b>Quantum machine learning algorithms based on linear algebra</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs092.html#quantum-reinforcement-learning" style="font-size: 80%;"><b>Quantum reinforcement learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs093.html#quantum-deep-learning" style="font-size: 80%;"><b>Quantum deep learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="#social-machine-learning" style="font-size: 80%;"><b>Social machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs095.html#the-last-words" style="font-size: 80%;"><b>The last words?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs096.html#ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean" style="font-size: 80%;"><b>AI/ML and some statements you may have heard (and what do they mean?)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs097.html#best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester" style="font-size: 80%;"><b>Best wishes to you all and thanks so much for your heroic efforts this semester</b></a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div> <!-- end of navigation bar -->
|
||||
<div class="container">
|
||||
<p> </p><p> </p><p> </p> <!-- add vertical space -->
|
||||
<a name="part0094"></a>
|
||||
<!-- !split -->
|
||||
<h2 id="social-machine-learning" class="anchor">Social machine learning </h2>
|
||||
|
||||
<p>Machine learning aims to imitate how humans
|
||||
learn. While we have developed successful machine learning algorithms,
|
||||
until now we have ignored one important fact: humans are social. Each
|
||||
of us is one part of the total society and it is difficult for us to
|
||||
live, learn, and improve ourselves, alone and isolated. Therefore, we
|
||||
should design machines with social properties. Can we let machines
|
||||
evolve by imitating human society so as to achieve more effective,
|
||||
intelligent, interpretable “social machine learning”?
|
||||
</p>
|
||||
|
||||
<p>And much more.</p>
|
||||
|
||||
<p>
|
||||
<!-- navigation buttons at the bottom of the page -->
|
||||
<ul class="pagination">
|
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<li><a href="._week47-bs093.html">«</a></li>
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<li><a href="._week47-bs000.html">1</a></li>
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<li><a href="">...</a></li>
|
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<li><a href="._week47-bs086.html">87</a></li>
|
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<li><a href="._week47-bs087.html">88</a></li>
|
||||
<li><a href="._week47-bs088.html">89</a></li>
|
||||
<li><a href="._week47-bs089.html">90</a></li>
|
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|
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<li class="active"><a href="._week47-bs094.html">95</a></li>
|
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<li><a href="._week47-bs095.html">96</a></li>
|
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<li><a href="._week47-bs096.html">97</a></li>
|
||||
<li><a href="._week47-bs097.html">98</a></li>
|
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|
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|
||||
('Basic ideas of the Principal Component Analysis (PCA)',
|
||||
2,
|
||||
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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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|
||||
("A kind of Bird's view on PCA",
|
||||
2,
|
||||
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|
||||
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|
||||
('Writing our own PCA code', 2, None, 'writing-our-own-pca-code'),
|
||||
('Implementing it', 2, None, 'implementing-it'),
|
||||
('First Step', 2, None, 'first-step'),
|
||||
('Scaling', 2, None, 'scaling'),
|
||||
('Centered Data', 2, None, 'centered-data'),
|
||||
('Exploring', 2, None, 'exploring'),
|
||||
('Diagonalize the sample covariance matrix to obtain the '
|
||||
'principal components',
|
||||
2,
|
||||
None,
|
||||
'diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components'),
|
||||
('Collecting all Steps', 2, None, 'collecting-all-steps'),
|
||||
('Classical PCA Theorem', 2, None, 'classical-pca-theorem'),
|
||||
('The PCA Theorem', 2, None, 'the-pca-theorem'),
|
||||
('Geometric Interpretation and link with Singular Value '
|
||||
'Decomposition',
|
||||
2,
|
||||
None,
|
||||
'geometric-interpretation-and-link-with-singular-value-decomposition'),
|
||||
('PCA and scikit-learn', 2, None, 'pca-and-scikit-learn'),
|
||||
('Back to the Cancer Data', 2, None, 'back-to-the-cancer-data'),
|
||||
('Incremental PCA', 2, None, 'incremental-pca'),
|
||||
('Randomized PCA', 3, None, 'randomized-pca'),
|
||||
('Kernel PCA', 3, None, 'kernel-pca'),
|
||||
('Other techniques', 2, None, 'other-techniques'),
|
||||
('Clustering and Unsupervised Learning',
|
||||
2,
|
||||
None,
|
||||
'clustering-and-unsupervised-learning'),
|
||||
('Basic Idea of the $k$-means Clustering Algorithm',
|
||||
2,
|
||||
None,
|
||||
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|
||||
('The $k$-means Algorithm', 2, None, 'the-k-means-algorithm'),
|
||||
('Basic Math of the $k$-means Algorithm',
|
||||
2,
|
||||
None,
|
||||
'basic-math-of-the-k-means-algorithm'),
|
||||
('Within Cluster Point Scatter',
|
||||
2,
|
||||
None,
|
||||
'within-cluster-point-scatter'),
|
||||
('More Details', 2, None, 'more-details'),
|
||||
('Total Cluster Variance', 2, None, 'total-cluster-variance'),
|
||||
('The $k$-means Clustering Algorithm',
|
||||
2,
|
||||
None,
|
||||
'the-k-means-clustering-algorithm'),
|
||||
('Summarizing', 2, None, 'summarizing'),
|
||||
('Writing our own Code, the Data Set',
|
||||
2,
|
||||
None,
|
||||
'writing-our-own-code-the-data-set'),
|
||||
('Implementing the $k$-means Algorithm',
|
||||
2,
|
||||
None,
|
||||
'implementing-the-k-means-algorithm'),
|
||||
('Plotting', 2, None, 'plotting'),
|
||||
('Continuing', 2, None, 'continuing'),
|
||||
('Wrapping it up', 2, None, 'wrapping-it-up'),
|
||||
('Summary of course', 2, None, 'summary-of-course'),
|
||||
('What? Me worry? No final exam in this course!',
|
||||
2,
|
||||
None,
|
||||
'what-me-worry-no-final-exam-in-this-course'),
|
||||
('What is the link between Artificial Intelligence and Machine '
|
||||
'Learning and some general Remarks',
|
||||
2,
|
||||
None,
|
||||
'what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks'),
|
||||
('Going back to the beginning of the semester',
|
||||
2,
|
||||
None,
|
||||
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|
||||
('Not so sharp distinctions',
|
||||
2,
|
||||
None,
|
||||
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|
||||
('Topics we have covered this year',
|
||||
2,
|
||||
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|
||||
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|
||||
('Statistical analysis and optimization of data',
|
||||
2,
|
||||
None,
|
||||
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|
||||
('Machine learning', 2, None, 'machine-learning'),
|
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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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|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
||||
2,
|
||||
None,
|
||||
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|
||||
('Optimization Methods and Hyperparameters',
|
||||
2,
|
||||
None,
|
||||
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|
||||
('Resampling', 2, None, 'resampling'),
|
||||
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|
||||
2,
|
||||
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|
||||
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|
||||
('Additional courses of interest',
|
||||
2,
|
||||
None,
|
||||
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|
||||
("What's the future like?", 2, None, 'what-s-the-future-like'),
|
||||
('Types of Machine Learning, a repetition',
|
||||
2,
|
||||
None,
|
||||
'types-of-machine-learning-a-repetition'),
|
||||
('Why Boltzmann machines?', 2, None, 'why-boltzmann-machines'),
|
||||
('Boltzmann Machines', 2, None, 'boltzmann-machines'),
|
||||
('Some similarities and differences from DNNs',
|
||||
2,
|
||||
None,
|
||||
'some-similarities-and-differences-from-dnns'),
|
||||
('Boltzmann machines (BM)', 2, None, 'boltzmann-machines-bm'),
|
||||
('A standard BM setup', 2, None, 'a-standard-bm-setup'),
|
||||
('The structure of the RBM network',
|
||||
2,
|
||||
None,
|
||||
'the-structure-of-the-rbm-network'),
|
||||
('The network', 2, None, 'the-network'),
|
||||
('Goals', 2, None, 'goals'),
|
||||
('Joint distribution', 2, None, 'joint-distribution'),
|
||||
('Network Elements, the energy function',
|
||||
2,
|
||||
None,
|
||||
'network-elements-the-energy-function'),
|
||||
('Defining different types of RBMs',
|
||||
2,
|
||||
None,
|
||||
'defining-different-types-of-rbms'),
|
||||
('More about RBMs', 2, None, 'more-about-rbms'),
|
||||
('Autoencoders: Overarching view',
|
||||
2,
|
||||
None,
|
||||
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|
||||
('Bayesian Machine Learning',
|
||||
2,
|
||||
None,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
2,
|
||||
None,
|
||||
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|
||||
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|
||||
('The Challenges Facing Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'the-challenges-facing-machine-learning'),
|
||||
('Explainable machine learning',
|
||||
2,
|
||||
None,
|
||||
'explainable-machine-learning'),
|
||||
('Scientific Machine Learning',
|
||||
2,
|
||||
None,
|
||||
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|
||||
('Quantum machine learning', 2, None, 'quantum-machine-learning'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'quantum-machine-learning-algorithms-based-on-linear-algebra'),
|
||||
('Quantum reinforcement learning',
|
||||
2,
|
||||
None,
|
||||
'quantum-reinforcement-learning'),
|
||||
('Quantum deep learning', 2, None, 'quantum-deep-learning'),
|
||||
('Social machine learning', 2, None, 'social-machine-learning'),
|
||||
('The last words?', 2, None, 'the-last-words'),
|
||||
('AI/ML and some statements you may have heard (and what do they '
|
||||
'mean?)',
|
||||
2,
|
||||
None,
|
||||
'ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester')]}
|
||||
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<a class="navbar-brand" href="week47-bs.html">Week 47: Unsupervised learning (PCA and Clustering) 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="._week47-bs001.html#overview-of-week-47" style="font-size: 80%;"><b>Overview of week 47</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs002.html#basic-ideas-of-the-principal-component-analysis-pca" style="font-size: 80%;"><b>Basic ideas of the Principal Component Analysis (PCA)</b></a></li>
|
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<!-- navigation toc: --> <li><a href="._week47-bs003.html#introducing-the-covariance-and-correlation-functions" style="font-size: 80%;"><b>Introducing the Covariance and Correlation functions</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs004.html#more-on-the-covariance" style="font-size: 80%;"><b>More on the covariance</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs005.html#reminding-ourselves-about-linear-regression" style="font-size: 80%;"><b>Reminding ourselves about Linear Regression</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs006.html#simple-example" style="font-size: 80%;"><b>Simple Example</b></a></li>
|
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<!-- navigation toc: --> <li><a href="._week47-bs007.html#the-correlation-matrix" style="font-size: 80%;"><b>The Correlation Matrix</b></a></li>
|
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<!-- navigation toc: --> <li><a href="._week47-bs008.html#numpy-functionality" style="font-size: 80%;"><b>Numpy Functionality</b></a></li>
|
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<!-- navigation toc: --> <li><a href="._week47-bs009.html#correlation-matrix-again" style="font-size: 80%;"><b>Correlation Matrix again</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs010.html#using-pandas" style="font-size: 80%;"><b>Using Pandas</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs011.html#and-then-the-franke-function" style="font-size: 80%;"><b>And then the Franke Function</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs012.html#links-with-the-design-matrix" style="font-size: 80%;"><b>Links with the Design Matrix</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs013.html#computing-the-expectation-values" style="font-size: 80%;"><b>Computing the Expectation Values</b></a></li>
|
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<!-- navigation toc: --> <li><a href="._week47-bs014.html#towards-the-pca-theorem" style="font-size: 80%;"><b>Towards the PCA theorem</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs015.html#more-on-the-pca-theorem" style="font-size: 80%;"><b>More on the PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs016.html#a-kind-of-bird-s-view-on-pca" style="font-size: 80%;"><b>A kind of Bird's view on PCA</b></a></li>
|
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<!-- navigation toc: --> <li><a href="._week47-bs017.html#writing-our-own-pca-code" style="font-size: 80%;"><b>Writing our own PCA code</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs018.html#implementing-it" style="font-size: 80%;"><b>Implementing it</b></a></li>
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||||
<!-- navigation toc: --> <li><a href="._week47-bs019.html#first-step" style="font-size: 80%;"><b>First Step</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs020.html#scaling" style="font-size: 80%;"><b>Scaling</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs021.html#centered-data" style="font-size: 80%;"><b>Centered Data</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs022.html#exploring" style="font-size: 80%;"><b>Exploring</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs023.html#diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components" style="font-size: 80%;"><b>Diagonalize the sample covariance matrix to obtain the principal components</b></a></li>
|
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<!-- navigation toc: --> <li><a href="._week47-bs024.html#collecting-all-steps" style="font-size: 80%;"><b>Collecting all Steps</b></a></li>
|
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<!-- navigation toc: --> <li><a href="._week47-bs025.html#classical-pca-theorem" style="font-size: 80%;"><b>Classical PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs026.html#the-pca-theorem" style="font-size: 80%;"><b>The PCA Theorem</b></a></li>
|
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<!-- navigation toc: --> <li><a href="._week47-bs027.html#geometric-interpretation-and-link-with-singular-value-decomposition" style="font-size: 80%;"><b>Geometric Interpretation and link with Singular Value Decomposition</b></a></li>
|
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<!-- navigation toc: --> <li><a href="._week47-bs028.html#pca-and-scikit-learn" style="font-size: 80%;"><b>PCA and scikit-learn</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs029.html#back-to-the-cancer-data" style="font-size: 80%;"><b>Back to the Cancer Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#incremental-pca" style="font-size: 80%;"><b>Incremental PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#randomized-pca" style="font-size: 80%;"> Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#kernel-pca" style="font-size: 80%;"> Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs031.html#other-techniques" style="font-size: 80%;"><b>Other techniques</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs032.html#clustering-and-unsupervised-learning" style="font-size: 80%;"><b>Clustering and Unsupervised Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs033.html#basic-idea-of-the-k-means-clustering-algorithm" style="font-size: 80%;"><b>Basic Idea of the \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs034.html#the-k-means-algorithm" style="font-size: 80%;"><b>The \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs035.html#basic-math-of-the-k-means-algorithm" style="font-size: 80%;"><b>Basic Math of the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs036.html#within-cluster-point-scatter" style="font-size: 80%;"><b>Within Cluster Point Scatter</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs037.html#more-details" style="font-size: 80%;"><b>More Details</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs038.html#total-cluster-variance" style="font-size: 80%;"><b>Total Cluster Variance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs039.html#the-k-means-clustering-algorithm" style="font-size: 80%;"><b>The \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs040.html#summarizing" style="font-size: 80%;"><b>Summarizing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs041.html#writing-our-own-code-the-data-set" style="font-size: 80%;"><b>Writing our own Code, the Data Set</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs042.html#implementing-the-k-means-algorithm" style="font-size: 80%;"><b>Implementing the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs043.html#plotting" style="font-size: 80%;"><b>Plotting</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs044.html#continuing" style="font-size: 80%;"><b>Continuing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs045.html#wrapping-it-up" style="font-size: 80%;"><b>Wrapping it up</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs046.html#summary-of-course" style="font-size: 80%;"><b>Summary of course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs047.html#what-me-worry-no-final-exam-in-this-course" style="font-size: 80%;"><b>What? Me worry? No final exam in this course!</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs048.html#what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks" style="font-size: 80%;"><b>What is the link between Artificial Intelligence and Machine Learning and some general Remarks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs049.html#going-back-to-the-beginning-of-the-semester" style="font-size: 80%;"><b>Going back to the beginning of the semester</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs050.html#not-so-sharp-distinctions" style="font-size: 80%;"><b>Not so sharp distinctions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs051.html#topics-we-have-covered-this-year" style="font-size: 80%;"><b>Topics we have covered this year</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs052.html#statistical-analysis-and-optimization-of-data" style="font-size: 80%;"><b>Statistical analysis and optimization of data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs053.html#machine-learning" style="font-size: 80%;"><b>Machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs054.html#learning-outcomes-and-overarching-aims-of-this-course" style="font-size: 80%;"><b>Learning outcomes and overarching aims of this course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs055.html#perspective-on-machine-learning" style="font-size: 80%;"><b>Perspective on Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs056.html#machine-learning-research" style="font-size: 80%;"><b>Machine Learning Research</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs057.html#starting-your-machine-learning-project" style="font-size: 80%;"><b>Starting your Machine Learning Project</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs058.html#choose-a-model-and-algorithm" style="font-size: 80%;"><b>Choose a Model and Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs059.html#preparing-your-data" style="font-size: 80%;"><b>Preparing Your Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs060.html#which-activation-and-weights-to-choose-in-neural-networks" style="font-size: 80%;"><b>Which Activation and Weights to Choose in Neural Networks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs061.html#optimization-methods-and-hyperparameters" style="font-size: 80%;"><b>Optimization Methods and Hyperparameters</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs062.html#resampling" style="font-size: 80%;"><b>Resampling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs063.html#other-courses-on-data-science-and-machine-learning-at-uio" style="font-size: 80%;"><b>Other courses on Data science and Machine Learning at UiO</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs064.html#additional-courses-of-interest" style="font-size: 80%;"><b>Additional courses of interest</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs065.html#what-s-the-future-like" style="font-size: 80%;"><b>What's the future like?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs066.html#types-of-machine-learning-a-repetition" style="font-size: 80%;"><b>Types of Machine Learning, a repetition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs067.html#why-boltzmann-machines" style="font-size: 80%;"><b>Why Boltzmann machines?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs068.html#boltzmann-machines" style="font-size: 80%;"><b>Boltzmann Machines</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs069.html#some-similarities-and-differences-from-dnns" style="font-size: 80%;"><b>Some similarities and differences from DNNs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs070.html#boltzmann-machines-bm" style="font-size: 80%;"><b>Boltzmann machines (BM)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs071.html#a-standard-bm-setup" style="font-size: 80%;"><b>A standard BM setup</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs072.html#the-structure-of-the-rbm-network" style="font-size: 80%;"><b>The structure of the RBM network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs073.html#the-network" style="font-size: 80%;"><b>The network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs074.html#goals" style="font-size: 80%;"><b>Goals</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs075.html#joint-distribution" style="font-size: 80%;"><b>Joint distribution</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs076.html#network-elements-the-energy-function" style="font-size: 80%;"><b>Network Elements, the energy function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs077.html#defining-different-types-of-rbms" style="font-size: 80%;"><b>Defining different types of RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs078.html#more-about-rbms" style="font-size: 80%;"><b>More about RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs079.html#autoencoders-overarching-view" style="font-size: 80%;"><b>Autoencoders: Overarching view</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs080.html#bayesian-machine-learning" style="font-size: 80%;"><b>Bayesian Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs081.html#reinforcement-learning" style="font-size: 80%;"><b>Reinforcement Learning</b></a></li>
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||||
<!-- navigation toc: --> <li><a href="._week47-bs082.html#transfer-learning" style="font-size: 80%;"><b>Transfer learning</b></a></li>
|
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<!-- navigation toc: --> <li><a href="._week47-bs083.html#adversarial-learning" style="font-size: 80%;"><b>Adversarial learning</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs084.html#dual-learning" style="font-size: 80%;"><b>Dual learning</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs085.html#distributed-machine-learning" style="font-size: 80%;"><b>Distributed machine learning</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs086.html#meta-learning" style="font-size: 80%;"><b>Meta learning</b></a></li>
|
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<!-- navigation toc: --> <li><a href="._week47-bs087.html#the-challenges-facing-machine-learning" style="font-size: 80%;"><b>The Challenges Facing Machine Learning</b></a></li>
|
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<!-- navigation toc: --> <li><a href="._week47-bs088.html#explainable-machine-learning" style="font-size: 80%;"><b>Explainable machine learning</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs089.html#scientific-machine-learning" style="font-size: 80%;"><b>Scientific Machine Learning</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs090.html#quantum-machine-learning" style="font-size: 80%;"><b>Quantum machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs091.html#quantum-machine-learning-algorithms-based-on-linear-algebra" style="font-size: 80%;"><b>Quantum machine learning algorithms based on linear algebra</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs092.html#quantum-reinforcement-learning" style="font-size: 80%;"><b>Quantum reinforcement learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs093.html#quantum-deep-learning" style="font-size: 80%;"><b>Quantum deep learning</b></a></li>
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<!-- navigation toc: --> <li><a href="#the-last-words" style="font-size: 80%;"><b>The last words?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs096.html#ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean" style="font-size: 80%;"><b>AI/ML and some statements you may have heard (and what do they mean?)</b></a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs097.html#best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester" style="font-size: 80%;"><b>Best wishes to you all and thanks so much for your heroic efforts this semester</b></a></li>
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</ul>
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<h2 id="the-last-words" class="anchor">The last words? </h2>
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<p>Early computer scientist Alan Kay said, <b>The best way to predict the
|
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future is to create it</b>. Therefore, all machine learning
|
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practitioners, whether scholars or engineers, professors or students,
|
||||
need to work together to advance these important research
|
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{'highest level': 2,
|
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'sections': [('Overview of week 47', 2, None, 'overview-of-week-47'),
|
||||
('Basic ideas of the Principal Component Analysis (PCA)',
|
||||
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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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|
||||
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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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('Other courses on Data science and Machine Learning at UiO',
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||||
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||||
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||||
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|
||||
('Additional courses of interest',
|
||||
2,
|
||||
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|
||||
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|
||||
("What's the future like?", 2, None, 'what-s-the-future-like'),
|
||||
('Types of Machine Learning, a repetition',
|
||||
2,
|
||||
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|
||||
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|
||||
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|
||||
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||||
('Some similarities and differences from DNNs',
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('The structure of the RBM network',
|
||||
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|
||||
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|
||||
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|
||||
('The network', 2, None, 'the-network'),
|
||||
('Goals', 2, None, 'goals'),
|
||||
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|
||||
('Network Elements, the energy function',
|
||||
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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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|
||||
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|
||||
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|
||||
None,
|
||||
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|
||||
('Quantum deep learning', 2, None, 'quantum-deep-learning'),
|
||||
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|
||||
('The last words?', 2, None, 'the-last-words'),
|
||||
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|
||||
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|
||||
2,
|
||||
None,
|
||||
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|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
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|
||||
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<a class="navbar-brand" href="week47-bs.html">Week 47: Unsupervised learning (PCA and Clustering) and Summary of Course</a>
|
||||
</div>
|
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<div class="navbar-collapse collapse navbar-responsive-collapse">
|
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<ul class="nav navbar-nav navbar-right">
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<li class="dropdown">
|
||||
<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
|
||||
<ul class="dropdown-menu">
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs001.html#overview-of-week-47" style="font-size: 80%;"><b>Overview of week 47</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs002.html#basic-ideas-of-the-principal-component-analysis-pca" style="font-size: 80%;"><b>Basic ideas of the Principal Component Analysis (PCA)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs003.html#introducing-the-covariance-and-correlation-functions" style="font-size: 80%;"><b>Introducing the Covariance and Correlation functions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs004.html#more-on-the-covariance" style="font-size: 80%;"><b>More on the covariance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs005.html#reminding-ourselves-about-linear-regression" style="font-size: 80%;"><b>Reminding ourselves about Linear Regression</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs006.html#simple-example" style="font-size: 80%;"><b>Simple Example</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs007.html#the-correlation-matrix" style="font-size: 80%;"><b>The Correlation Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs008.html#numpy-functionality" style="font-size: 80%;"><b>Numpy Functionality</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs009.html#correlation-matrix-again" style="font-size: 80%;"><b>Correlation Matrix again</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs010.html#using-pandas" style="font-size: 80%;"><b>Using Pandas</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs011.html#and-then-the-franke-function" style="font-size: 80%;"><b>And then the Franke Function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs012.html#links-with-the-design-matrix" style="font-size: 80%;"><b>Links with the Design Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs013.html#computing-the-expectation-values" style="font-size: 80%;"><b>Computing the Expectation Values</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs014.html#towards-the-pca-theorem" style="font-size: 80%;"><b>Towards the PCA theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs015.html#more-on-the-pca-theorem" style="font-size: 80%;"><b>More on the PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs016.html#a-kind-of-bird-s-view-on-pca" style="font-size: 80%;"><b>A kind of Bird's view on PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs017.html#writing-our-own-pca-code" style="font-size: 80%;"><b>Writing our own PCA code</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs018.html#implementing-it" style="font-size: 80%;"><b>Implementing it</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs019.html#first-step" style="font-size: 80%;"><b>First Step</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs020.html#scaling" style="font-size: 80%;"><b>Scaling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs021.html#centered-data" style="font-size: 80%;"><b>Centered Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs022.html#exploring" style="font-size: 80%;"><b>Exploring</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs023.html#diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components" style="font-size: 80%;"><b>Diagonalize the sample covariance matrix to obtain the principal components</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs024.html#collecting-all-steps" style="font-size: 80%;"><b>Collecting all Steps</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs025.html#classical-pca-theorem" style="font-size: 80%;"><b>Classical PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs026.html#the-pca-theorem" style="font-size: 80%;"><b>The PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs027.html#geometric-interpretation-and-link-with-singular-value-decomposition" style="font-size: 80%;"><b>Geometric Interpretation and link with Singular Value Decomposition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs028.html#pca-and-scikit-learn" style="font-size: 80%;"><b>PCA and scikit-learn</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs029.html#back-to-the-cancer-data" style="font-size: 80%;"><b>Back to the Cancer Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#incremental-pca" style="font-size: 80%;"><b>Incremental PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#randomized-pca" style="font-size: 80%;"> Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#kernel-pca" style="font-size: 80%;"> Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs031.html#other-techniques" style="font-size: 80%;"><b>Other techniques</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs032.html#clustering-and-unsupervised-learning" style="font-size: 80%;"><b>Clustering and Unsupervised Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs033.html#basic-idea-of-the-k-means-clustering-algorithm" style="font-size: 80%;"><b>Basic Idea of the \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs034.html#the-k-means-algorithm" style="font-size: 80%;"><b>The \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs035.html#basic-math-of-the-k-means-algorithm" style="font-size: 80%;"><b>Basic Math of the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs036.html#within-cluster-point-scatter" style="font-size: 80%;"><b>Within Cluster Point Scatter</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs037.html#more-details" style="font-size: 80%;"><b>More Details</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs038.html#total-cluster-variance" style="font-size: 80%;"><b>Total Cluster Variance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs039.html#the-k-means-clustering-algorithm" style="font-size: 80%;"><b>The \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs040.html#summarizing" style="font-size: 80%;"><b>Summarizing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs041.html#writing-our-own-code-the-data-set" style="font-size: 80%;"><b>Writing our own Code, the Data Set</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs042.html#implementing-the-k-means-algorithm" style="font-size: 80%;"><b>Implementing the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs043.html#plotting" style="font-size: 80%;"><b>Plotting</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs044.html#continuing" style="font-size: 80%;"><b>Continuing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs045.html#wrapping-it-up" style="font-size: 80%;"><b>Wrapping it up</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs046.html#summary-of-course" style="font-size: 80%;"><b>Summary of course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs047.html#what-me-worry-no-final-exam-in-this-course" style="font-size: 80%;"><b>What? Me worry? No final exam in this course!</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs048.html#what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks" style="font-size: 80%;"><b>What is the link between Artificial Intelligence and Machine Learning and some general Remarks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs049.html#going-back-to-the-beginning-of-the-semester" style="font-size: 80%;"><b>Going back to the beginning of the semester</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs050.html#not-so-sharp-distinctions" style="font-size: 80%;"><b>Not so sharp distinctions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs051.html#topics-we-have-covered-this-year" style="font-size: 80%;"><b>Topics we have covered this year</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs052.html#statistical-analysis-and-optimization-of-data" style="font-size: 80%;"><b>Statistical analysis and optimization of data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs053.html#machine-learning" style="font-size: 80%;"><b>Machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs054.html#learning-outcomes-and-overarching-aims-of-this-course" style="font-size: 80%;"><b>Learning outcomes and overarching aims of this course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs055.html#perspective-on-machine-learning" style="font-size: 80%;"><b>Perspective on Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs056.html#machine-learning-research" style="font-size: 80%;"><b>Machine Learning Research</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs057.html#starting-your-machine-learning-project" style="font-size: 80%;"><b>Starting your Machine Learning Project</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs058.html#choose-a-model-and-algorithm" style="font-size: 80%;"><b>Choose a Model and Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs059.html#preparing-your-data" style="font-size: 80%;"><b>Preparing Your Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs060.html#which-activation-and-weights-to-choose-in-neural-networks" style="font-size: 80%;"><b>Which Activation and Weights to Choose in Neural Networks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs061.html#optimization-methods-and-hyperparameters" style="font-size: 80%;"><b>Optimization Methods and Hyperparameters</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs062.html#resampling" style="font-size: 80%;"><b>Resampling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs063.html#other-courses-on-data-science-and-machine-learning-at-uio" style="font-size: 80%;"><b>Other courses on Data science and Machine Learning at UiO</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs064.html#additional-courses-of-interest" style="font-size: 80%;"><b>Additional courses of interest</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs065.html#what-s-the-future-like" style="font-size: 80%;"><b>What's the future like?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs066.html#types-of-machine-learning-a-repetition" style="font-size: 80%;"><b>Types of Machine Learning, a repetition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs067.html#why-boltzmann-machines" style="font-size: 80%;"><b>Why Boltzmann machines?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs068.html#boltzmann-machines" style="font-size: 80%;"><b>Boltzmann Machines</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs069.html#some-similarities-and-differences-from-dnns" style="font-size: 80%;"><b>Some similarities and differences from DNNs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs070.html#boltzmann-machines-bm" style="font-size: 80%;"><b>Boltzmann machines (BM)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs071.html#a-standard-bm-setup" style="font-size: 80%;"><b>A standard BM setup</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs072.html#the-structure-of-the-rbm-network" style="font-size: 80%;"><b>The structure of the RBM network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs073.html#the-network" style="font-size: 80%;"><b>The network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs074.html#goals" style="font-size: 80%;"><b>Goals</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs075.html#joint-distribution" style="font-size: 80%;"><b>Joint distribution</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs076.html#network-elements-the-energy-function" style="font-size: 80%;"><b>Network Elements, the energy function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs077.html#defining-different-types-of-rbms" style="font-size: 80%;"><b>Defining different types of RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs078.html#more-about-rbms" style="font-size: 80%;"><b>More about RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs079.html#autoencoders-overarching-view" style="font-size: 80%;"><b>Autoencoders: Overarching view</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs080.html#bayesian-machine-learning" style="font-size: 80%;"><b>Bayesian Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs081.html#reinforcement-learning" style="font-size: 80%;"><b>Reinforcement Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs082.html#transfer-learning" style="font-size: 80%;"><b>Transfer learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs083.html#adversarial-learning" style="font-size: 80%;"><b>Adversarial learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs084.html#dual-learning" style="font-size: 80%;"><b>Dual learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs085.html#distributed-machine-learning" style="font-size: 80%;"><b>Distributed machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs086.html#meta-learning" style="font-size: 80%;"><b>Meta learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs087.html#the-challenges-facing-machine-learning" style="font-size: 80%;"><b>The Challenges Facing Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs088.html#explainable-machine-learning" style="font-size: 80%;"><b>Explainable machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs089.html#scientific-machine-learning" style="font-size: 80%;"><b>Scientific Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs090.html#quantum-machine-learning" style="font-size: 80%;"><b>Quantum machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs091.html#quantum-machine-learning-algorithms-based-on-linear-algebra" style="font-size: 80%;"><b>Quantum machine learning algorithms based on linear algebra</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs092.html#quantum-reinforcement-learning" style="font-size: 80%;"><b>Quantum reinforcement learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs093.html#quantum-deep-learning" style="font-size: 80%;"><b>Quantum deep learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs094.html#social-machine-learning" style="font-size: 80%;"><b>Social machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs095.html#the-last-words" style="font-size: 80%;"><b>The last words?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="#ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean" style="font-size: 80%;"><b>AI/ML and some statements you may have heard (and what do they mean?)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs097.html#best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester" style="font-size: 80%;"><b>Best wishes to you all and thanks so much for your heroic efforts this semester</b></a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div> <!-- end of navigation bar -->
|
||||
<div class="container">
|
||||
<p> </p><p> </p><p> </p> <!-- add vertical space -->
|
||||
<a name="part0096"></a>
|
||||
<!-- !split -->
|
||||
<h2 id="ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean" class="anchor">AI/ML and some statements you may have heard (and what do they mean?) </h2>
|
||||
|
||||
<ol>
|
||||
<li> Fei-Fei Li on ImageNet: <b>map out the entire world of objects</b> (<a href="https://cacm.acm.org/news/219702-the-data-that-transformed-ai-research-and-possibly-the-world/fulltext" target="_self">The data that transformed AI research</a>)</li>
|
||||
<li> Russell and Norvig in their popular textbook: <b>relevant to any intellectual task; it is truly a universal field</b> (<a href="http://aima.cs.berkeley.edu/" target="_self">Artificial Intelligence, A modern approach</a>)</li>
|
||||
<li> Woody Bledsoe puts it more bluntly: <b>in the long run, AI is the only science</b> (quoted in Pamilla McCorduck, <a href="https://www.pamelamccorduck.com/machines-who-think" target="_self">Machines who think</a>)</li>
|
||||
</ol>
|
||||
<p>If you wish to have a critical read on AI/ML from a societal point of view, see <a href="https://www.katecrawford.net/" target="_self">Kate Crawford's recent text Atlas of AI</a></p>
|
||||
|
||||
<b>Here: with AI/ML we intend a collection of machine learning methods with an emphasis on statistical learning and data analysis</b>
|
||||
|
||||
<p>
|
||||
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|
||||
<ul class="pagination">
|
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<li><a href="._week47-bs095.html">«</a></li>
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<li><a href="">...</a></li>
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<li><a href="._week47-bs088.html">89</a></li>
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|
||||
('Basic ideas of the Principal Component Analysis (PCA)',
|
||||
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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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|
||||
("A kind of Bird's view on PCA",
|
||||
2,
|
||||
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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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|
||||
('Diagonalize the sample covariance matrix to obtain the '
|
||||
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|
||||
2,
|
||||
None,
|
||||
'diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components'),
|
||||
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|
||||
('Classical PCA Theorem', 2, None, 'classical-pca-theorem'),
|
||||
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|
||||
('Geometric Interpretation and link with Singular Value '
|
||||
'Decomposition',
|
||||
2,
|
||||
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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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|
||||
('Clustering and Unsupervised Learning',
|
||||
2,
|
||||
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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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|
||||
('Basic Math of the $k$-means Algorithm',
|
||||
2,
|
||||
None,
|
||||
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|
||||
('Within Cluster Point Scatter',
|
||||
2,
|
||||
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|
||||
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|
||||
('More Details', 2, None, 'more-details'),
|
||||
('Total Cluster Variance', 2, None, 'total-cluster-variance'),
|
||||
('The $k$-means Clustering Algorithm',
|
||||
2,
|
||||
None,
|
||||
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|
||||
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|
||||
('Writing our own Code, the Data Set',
|
||||
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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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|
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('What is the link between Artificial Intelligence and Machine '
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|
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|
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|
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|
||||
('Preparing Your Data', 2, None, 'preparing-your-data'),
|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
||||
2,
|
||||
None,
|
||||
'which-activation-and-weights-to-choose-in-neural-networks'),
|
||||
('Optimization Methods and Hyperparameters',
|
||||
2,
|
||||
None,
|
||||
'optimization-methods-and-hyperparameters'),
|
||||
('Resampling', 2, None, 'resampling'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
||||
'other-courses-on-data-science-and-machine-learning-at-uio'),
|
||||
('Additional courses of interest',
|
||||
2,
|
||||
None,
|
||||
'additional-courses-of-interest'),
|
||||
("What's the future like?", 2, None, 'what-s-the-future-like'),
|
||||
('Types of Machine Learning, a repetition',
|
||||
2,
|
||||
None,
|
||||
'types-of-machine-learning-a-repetition'),
|
||||
('Why Boltzmann machines?', 2, None, 'why-boltzmann-machines'),
|
||||
('Boltzmann Machines', 2, None, 'boltzmann-machines'),
|
||||
('Some similarities and differences from DNNs',
|
||||
2,
|
||||
None,
|
||||
'some-similarities-and-differences-from-dnns'),
|
||||
('Boltzmann machines (BM)', 2, None, 'boltzmann-machines-bm'),
|
||||
('A standard BM setup', 2, None, 'a-standard-bm-setup'),
|
||||
('The structure of the RBM network',
|
||||
2,
|
||||
None,
|
||||
'the-structure-of-the-rbm-network'),
|
||||
('The network', 2, None, 'the-network'),
|
||||
('Goals', 2, None, 'goals'),
|
||||
('Joint distribution', 2, None, 'joint-distribution'),
|
||||
('Network Elements, the energy function',
|
||||
2,
|
||||
None,
|
||||
'network-elements-the-energy-function'),
|
||||
('Defining different types of RBMs',
|
||||
2,
|
||||
None,
|
||||
'defining-different-types-of-rbms'),
|
||||
('More about RBMs', 2, None, 'more-about-rbms'),
|
||||
('Autoencoders: Overarching view',
|
||||
2,
|
||||
None,
|
||||
'autoencoders-overarching-view'),
|
||||
('Bayesian Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'bayesian-machine-learning'),
|
||||
('Reinforcement Learning', 2, None, 'reinforcement-learning'),
|
||||
('Transfer learning', 2, None, 'transfer-learning'),
|
||||
('Adversarial learning', 2, None, 'adversarial-learning'),
|
||||
('Dual learning', 2, None, 'dual-learning'),
|
||||
('Distributed machine learning',
|
||||
2,
|
||||
None,
|
||||
'distributed-machine-learning'),
|
||||
('Meta learning', 2, None, 'meta-learning'),
|
||||
('The Challenges Facing Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'the-challenges-facing-machine-learning'),
|
||||
('Explainable machine learning',
|
||||
2,
|
||||
None,
|
||||
'explainable-machine-learning'),
|
||||
('Scientific Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'scientific-machine-learning'),
|
||||
('Quantum machine learning', 2, None, 'quantum-machine-learning'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'quantum-machine-learning-algorithms-based-on-linear-algebra'),
|
||||
('Quantum reinforcement learning',
|
||||
2,
|
||||
None,
|
||||
'quantum-reinforcement-learning'),
|
||||
('Quantum deep learning', 2, None, 'quantum-deep-learning'),
|
||||
('Social machine learning', 2, None, 'social-machine-learning'),
|
||||
('The last words?', 2, None, 'the-last-words'),
|
||||
('AI/ML and some statements you may have heard (and what do they '
|
||||
'mean?)',
|
||||
2,
|
||||
None,
|
||||
'ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester')]}
|
||||
end of tocinfo -->
|
||||
|
||||
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|
||||
|
||||
|
||||
|
||||
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|
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|
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<a class="navbar-brand" href="week47-bs.html">Week 47: Unsupervised learning (PCA and Clustering) and Summary of Course</a>
|
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|
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|
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<ul class="nav navbar-nav navbar-right">
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<li class="dropdown">
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<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
|
||||
<ul class="dropdown-menu">
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs001.html#overview-of-week-47" style="font-size: 80%;"><b>Overview of week 47</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs002.html#basic-ideas-of-the-principal-component-analysis-pca" style="font-size: 80%;"><b>Basic ideas of the Principal Component Analysis (PCA)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs003.html#introducing-the-covariance-and-correlation-functions" style="font-size: 80%;"><b>Introducing the Covariance and Correlation functions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs004.html#more-on-the-covariance" style="font-size: 80%;"><b>More on the covariance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs005.html#reminding-ourselves-about-linear-regression" style="font-size: 80%;"><b>Reminding ourselves about Linear Regression</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs006.html#simple-example" style="font-size: 80%;"><b>Simple Example</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs007.html#the-correlation-matrix" style="font-size: 80%;"><b>The Correlation Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs008.html#numpy-functionality" style="font-size: 80%;"><b>Numpy Functionality</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs009.html#correlation-matrix-again" style="font-size: 80%;"><b>Correlation Matrix again</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs010.html#using-pandas" style="font-size: 80%;"><b>Using Pandas</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs011.html#and-then-the-franke-function" style="font-size: 80%;"><b>And then the Franke Function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs012.html#links-with-the-design-matrix" style="font-size: 80%;"><b>Links with the Design Matrix</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs013.html#computing-the-expectation-values" style="font-size: 80%;"><b>Computing the Expectation Values</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs014.html#towards-the-pca-theorem" style="font-size: 80%;"><b>Towards the PCA theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs015.html#more-on-the-pca-theorem" style="font-size: 80%;"><b>More on the PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs016.html#a-kind-of-bird-s-view-on-pca" style="font-size: 80%;"><b>A kind of Bird's view on PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs017.html#writing-our-own-pca-code" style="font-size: 80%;"><b>Writing our own PCA code</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs018.html#implementing-it" style="font-size: 80%;"><b>Implementing it</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs019.html#first-step" style="font-size: 80%;"><b>First Step</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs020.html#scaling" style="font-size: 80%;"><b>Scaling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs021.html#centered-data" style="font-size: 80%;"><b>Centered Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs022.html#exploring" style="font-size: 80%;"><b>Exploring</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs023.html#diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components" style="font-size: 80%;"><b>Diagonalize the sample covariance matrix to obtain the principal components</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs024.html#collecting-all-steps" style="font-size: 80%;"><b>Collecting all Steps</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs025.html#classical-pca-theorem" style="font-size: 80%;"><b>Classical PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs026.html#the-pca-theorem" style="font-size: 80%;"><b>The PCA Theorem</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs027.html#geometric-interpretation-and-link-with-singular-value-decomposition" style="font-size: 80%;"><b>Geometric Interpretation and link with Singular Value Decomposition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs028.html#pca-and-scikit-learn" style="font-size: 80%;"><b>PCA and scikit-learn</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs029.html#back-to-the-cancer-data" style="font-size: 80%;"><b>Back to the Cancer Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#incremental-pca" style="font-size: 80%;"><b>Incremental PCA</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#randomized-pca" style="font-size: 80%;"> Randomized PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs030.html#kernel-pca" style="font-size: 80%;"> Kernel PCA</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs031.html#other-techniques" style="font-size: 80%;"><b>Other techniques</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs032.html#clustering-and-unsupervised-learning" style="font-size: 80%;"><b>Clustering and Unsupervised Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs033.html#basic-idea-of-the-k-means-clustering-algorithm" style="font-size: 80%;"><b>Basic Idea of the \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs034.html#the-k-means-algorithm" style="font-size: 80%;"><b>The \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs035.html#basic-math-of-the-k-means-algorithm" style="font-size: 80%;"><b>Basic Math of the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs036.html#within-cluster-point-scatter" style="font-size: 80%;"><b>Within Cluster Point Scatter</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs037.html#more-details" style="font-size: 80%;"><b>More Details</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs038.html#total-cluster-variance" style="font-size: 80%;"><b>Total Cluster Variance</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs039.html#the-k-means-clustering-algorithm" style="font-size: 80%;"><b>The \( k \)-means Clustering Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs040.html#summarizing" style="font-size: 80%;"><b>Summarizing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs041.html#writing-our-own-code-the-data-set" style="font-size: 80%;"><b>Writing our own Code, the Data Set</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs042.html#implementing-the-k-means-algorithm" style="font-size: 80%;"><b>Implementing the \( k \)-means Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs043.html#plotting" style="font-size: 80%;"><b>Plotting</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs044.html#continuing" style="font-size: 80%;"><b>Continuing</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs045.html#wrapping-it-up" style="font-size: 80%;"><b>Wrapping it up</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs046.html#summary-of-course" style="font-size: 80%;"><b>Summary of course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs047.html#what-me-worry-no-final-exam-in-this-course" style="font-size: 80%;"><b>What? Me worry? No final exam in this course!</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs048.html#what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks" style="font-size: 80%;"><b>What is the link between Artificial Intelligence and Machine Learning and some general Remarks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs049.html#going-back-to-the-beginning-of-the-semester" style="font-size: 80%;"><b>Going back to the beginning of the semester</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs050.html#not-so-sharp-distinctions" style="font-size: 80%;"><b>Not so sharp distinctions</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs051.html#topics-we-have-covered-this-year" style="font-size: 80%;"><b>Topics we have covered this year</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs052.html#statistical-analysis-and-optimization-of-data" style="font-size: 80%;"><b>Statistical analysis and optimization of data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs053.html#machine-learning" style="font-size: 80%;"><b>Machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs054.html#learning-outcomes-and-overarching-aims-of-this-course" style="font-size: 80%;"><b>Learning outcomes and overarching aims of this course</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs055.html#perspective-on-machine-learning" style="font-size: 80%;"><b>Perspective on Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs056.html#machine-learning-research" style="font-size: 80%;"><b>Machine Learning Research</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs057.html#starting-your-machine-learning-project" style="font-size: 80%;"><b>Starting your Machine Learning Project</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs058.html#choose-a-model-and-algorithm" style="font-size: 80%;"><b>Choose a Model and Algorithm</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs059.html#preparing-your-data" style="font-size: 80%;"><b>Preparing Your Data</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs060.html#which-activation-and-weights-to-choose-in-neural-networks" style="font-size: 80%;"><b>Which Activation and Weights to Choose in Neural Networks</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs061.html#optimization-methods-and-hyperparameters" style="font-size: 80%;"><b>Optimization Methods and Hyperparameters</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs062.html#resampling" style="font-size: 80%;"><b>Resampling</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs063.html#other-courses-on-data-science-and-machine-learning-at-uio" style="font-size: 80%;"><b>Other courses on Data science and Machine Learning at UiO</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs064.html#additional-courses-of-interest" style="font-size: 80%;"><b>Additional courses of interest</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs065.html#what-s-the-future-like" style="font-size: 80%;"><b>What's the future like?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs066.html#types-of-machine-learning-a-repetition" style="font-size: 80%;"><b>Types of Machine Learning, a repetition</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs067.html#why-boltzmann-machines" style="font-size: 80%;"><b>Why Boltzmann machines?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs068.html#boltzmann-machines" style="font-size: 80%;"><b>Boltzmann Machines</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs069.html#some-similarities-and-differences-from-dnns" style="font-size: 80%;"><b>Some similarities and differences from DNNs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs070.html#boltzmann-machines-bm" style="font-size: 80%;"><b>Boltzmann machines (BM)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs071.html#a-standard-bm-setup" style="font-size: 80%;"><b>A standard BM setup</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs072.html#the-structure-of-the-rbm-network" style="font-size: 80%;"><b>The structure of the RBM network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs073.html#the-network" style="font-size: 80%;"><b>The network</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs074.html#goals" style="font-size: 80%;"><b>Goals</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs075.html#joint-distribution" style="font-size: 80%;"><b>Joint distribution</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs076.html#network-elements-the-energy-function" style="font-size: 80%;"><b>Network Elements, the energy function</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs077.html#defining-different-types-of-rbms" style="font-size: 80%;"><b>Defining different types of RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs078.html#more-about-rbms" style="font-size: 80%;"><b>More about RBMs</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs079.html#autoencoders-overarching-view" style="font-size: 80%;"><b>Autoencoders: Overarching view</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs080.html#bayesian-machine-learning" style="font-size: 80%;"><b>Bayesian Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs081.html#reinforcement-learning" style="font-size: 80%;"><b>Reinforcement Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs082.html#transfer-learning" style="font-size: 80%;"><b>Transfer learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs083.html#adversarial-learning" style="font-size: 80%;"><b>Adversarial learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs084.html#dual-learning" style="font-size: 80%;"><b>Dual learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs085.html#distributed-machine-learning" style="font-size: 80%;"><b>Distributed machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs086.html#meta-learning" style="font-size: 80%;"><b>Meta learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs087.html#the-challenges-facing-machine-learning" style="font-size: 80%;"><b>The Challenges Facing Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs088.html#explainable-machine-learning" style="font-size: 80%;"><b>Explainable machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs089.html#scientific-machine-learning" style="font-size: 80%;"><b>Scientific Machine Learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs090.html#quantum-machine-learning" style="font-size: 80%;"><b>Quantum machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs091.html#quantum-machine-learning-algorithms-based-on-linear-algebra" style="font-size: 80%;"><b>Quantum machine learning algorithms based on linear algebra</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs092.html#quantum-reinforcement-learning" style="font-size: 80%;"><b>Quantum reinforcement learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs093.html#quantum-deep-learning" style="font-size: 80%;"><b>Quantum deep learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs094.html#social-machine-learning" style="font-size: 80%;"><b>Social machine learning</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs095.html#the-last-words" style="font-size: 80%;"><b>The last words?</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="._week47-bs096.html#ai-ml-and-some-statements-you-may-have-heard-and-what-do-they-mean" style="font-size: 80%;"><b>AI/ML and some statements you may have heard (and what do they mean?)</b></a></li>
|
||||
<!-- navigation toc: --> <li><a href="#best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester" style="font-size: 80%;"><b>Best wishes to you all and thanks so much for your heroic efforts this semester</b></a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div> <!-- end of navigation bar -->
|
||||
<div class="container">
|
||||
<p> </p><p> </p><p> </p> <!-- add vertical space -->
|
||||
<a name="part0097"></a>
|
||||
<!-- !split -->
|
||||
<h2 id="best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester" class="anchor">Best wishes to you all and thanks so much for your heroic efforts this semester </h2>
|
||||
|
||||
<br/><br/>
|
||||
<center>
|
||||
<p><img src="figures/Nebbdyr2.png" width="500" align="bottom"></p>
|
||||
</center>
|
||||
<br/><br/>
|
||||
|
||||
<p>
|
||||
<!-- navigation buttons at the bottom of the page -->
|
||||
<ul class="pagination">
|
||||
<li><a href="._week47-bs096.html">«</a></li>
|
||||
<li><a href="._week47-bs000.html">1</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._week47-bs089.html">90</a></li>
|
||||
<li><a href="._week47-bs090.html">91</a></li>
|
||||
<li><a href="._week47-bs091.html">92</a></li>
|
||||
<li><a href="._week47-bs092.html">93</a></li>
|
||||
<li><a href="._week47-bs093.html">94</a></li>
|
||||
<li><a href="._week47-bs094.html">95</a></li>
|
||||
<li><a href="._week47-bs095.html">96</a></li>
|
||||
<li><a href="._week47-bs096.html">97</a></li>
|
||||
<li class="active"><a href="._week47-bs097.html">98</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
</div> <!-- end container -->
|
||||
<!-- include javascript, jQuery *first* -->
|
||||
<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.10.2/jquery.min.js"></script>
|
||||
<script src="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/js/bootstrap.min.js"></script>
|
||||
<!-- Bootstrap footer
|
||||
<footer>
|
||||
<a href="https://..."><img width="250" align=right src="https://..."></a>
|
||||
</footer>
|
||||
-->
|
||||
<center style="font-size:80%">
|
||||
<!-- copyright only on the titlepage -->
|
||||
</center>
|
||||
</body>
|
||||
</html>
|
||||
|
||||
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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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3 -> 4 ;
|
||||
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|
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3 -> 5 ;
|
||||
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5 -> 6 ;
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5 -> 7 ;
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|
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2 -> 8 ;
|
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|
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8 -> 9 ;
|
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10 [label="gini = 0.0\nsamples = 3\nvalue = [[0, 3]\n[3, 0]]", fillcolor="#e58139ff"] ;
|
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8 -> 10 ;
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11 -> 12 ;
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|
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|
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14 -> 15 ;
|
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15 -> 16 ;
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15 -> 17 ;
|
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17 -> 18 ;
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|
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17 -> 19 ;
|
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20 [label="mean concave points <= 0.049\ngini = 0.088\nsamples = 151\nvalue = [[7, 144]\n[144, 7]]", fillcolor="#e58139d0"] ;
|
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14 -> 20 ;
|
||||
21 [label="concave points error <= 0.01\ngini = 0.48\nsamples = 15\nvalue = [[6, 9]\n[9, 6]]", fillcolor="#e5813900"] ;
|
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20 -> 21 ;
|
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21 -> 22 ;
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|
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21 -> 23 ;
|
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24 [label="worst smoothness <= 0.096\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e58139f7"] ;
|
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20 -> 24 ;
|
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25 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139ff"] ;
|
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24 -> 25 ;
|
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Grade Trend,Hours slept,Hours Studied,Grade
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1,0,1,1
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0,1,0,0
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1,0,1,1
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1,1,1,1
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0,0,1,0
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1,1,1,1
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Grade Trend,Hours slept,Hours Studied,Grade
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1 , 0 , 1 , 1
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0 , 1 , 0 , 0
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1 , 0 , 1 , 1
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1 , 1 , 1 , 1
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0 , 0 , 1 , 0
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1 , 0 , 0 , 0
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0 , 1 , 1 , 0
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1 , 0 , 0 , 0
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1 , 1 , 1 , 1
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Outlook,Temperature,Humidity,Wind,Ride
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0,0,0,0,0
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0,0,0,1,1
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1,0,0,0,1
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2,1,0,0,1
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2,2,1,0,1
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2,2,1,1,0
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1,2,1,1,1
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0,1,0,0,0
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0,2,1,0,1
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2,1,1,0,1
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0,1,1,1,1
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1,1,0,1,1
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1,0,1,0,1
|
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2,1,0,1,0
|
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Outlook,Temperature,Humidity,Wind,Ride
|
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Sunny,Hot,High,Weak,0
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Sunny,Hot,High,Strong,1
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Overcast,Hot,High,Weak,1
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Rain,Mild,High,Weak,1
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Rain,Cool,Normal,Weak,1
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Rain,Cool,Normal,Strong,0
|
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Overcast,Cool,Normal,Strong,1
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Sunny,Mild,High,Weak,0
|
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Sunny,Cool,Normal,Weak,1
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Rain,Mild,Normal,Weak,1
|
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Sunny,Mild,Normal,Strong,1
|
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Overcast,Mild,High,Strong,1
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Overcast,Hot,Normal,Weak,1
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Rain,Mild,High,Strong,0
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Day,Outlook,Temperature,Humidity,Wind,Ride
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1,Sunny,Hot,High,Weak,0
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aardvark,1,0,0,1,0,0,1,1,1,1,0,0,4,0,0,1,1
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antelope,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1
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bass,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,4
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bear,1,0,0,1,0,0,1,1,1,1,0,0,4,0,0,1,1
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boar,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
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carp,0,0,1,0,0,1,0,1,1,0,0,1,0,1,1,0,4
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cavy,1,0,0,1,0,0,0,1,1,1,0,0,4,0,1,0,1
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cheetah,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
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chicken,0,1,1,0,1,0,0,0,1,1,0,0,2,1,1,0,2
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chub,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,4
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clam,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,7
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crab,0,0,1,0,0,1,1,0,0,0,0,0,4,0,0,0,7
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crayfish,0,0,1,0,0,1,1,0,0,0,0,0,6,0,0,0,7
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crow,0,1,1,0,1,0,1,0,1,1,0,0,2,1,0,0,2
|
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deer,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1
|
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dogfish,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,1,4
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dove,0,1,1,0,1,0,0,0,1,1,0,0,2,1,1,0,2
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duck,0,1,1,0,1,1,0,0,1,1,0,0,2,1,0,0,2
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elephant,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1
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flamingo,0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,1,2
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flea,0,0,1,0,0,0,0,0,0,1,0,0,6,0,0,0,6
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frog,0,0,1,0,0,1,1,1,1,1,0,0,4,0,0,0,5
|
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frog,0,0,1,0,0,1,1,1,1,1,1,0,4,0,0,0,5
|
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fruitbat,1,0,0,1,1,0,0,1,1,1,0,0,2,1,0,0,1
|
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giraffe,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1
|
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girl,1,0,0,1,0,0,1,1,1,1,0,0,2,0,1,1,1
|
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gnat,0,0,1,0,1,0,0,0,0,1,0,0,6,0,0,0,6
|
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goat,1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,1,1
|
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gorilla,1,0,0,1,0,0,0,1,1,1,0,0,2,0,0,1,1
|
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gull,0,1,1,0,1,1,1,0,1,1,0,0,2,1,0,0,2
|
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haddock,0,0,1,0,0,1,0,1,1,0,0,1,0,1,0,0,4
|
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hamster,1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,0,1
|
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hare,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,0,1
|
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hawk,0,1,1,0,1,0,1,0,1,1,0,0,2,1,0,0,2
|
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herring,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,4
|
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honeybee,1,0,1,0,1,0,0,0,0,1,1,0,6,0,1,0,6
|
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housefly,1,0,1,0,1,0,0,0,0,1,0,0,6,0,0,0,6
|
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kiwi,0,1,1,0,0,0,1,0,1,1,0,0,2,1,0,0,2
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ladybird,0,0,1,0,1,0,1,0,0,1,0,0,6,0,0,0,6
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lark,0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,0,2
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leopard,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
|
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lion,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
|
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lobster,0,0,1,0,0,1,1,0,0,0,0,0,6,0,0,0,7
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mink,1,0,0,1,0,1,1,1,1,1,0,0,4,1,0,1,1
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mole,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,0,1
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mongoose,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
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moth,1,0,1,0,1,0,0,0,0,1,0,0,6,0,0,0,6
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newt,0,0,1,0,0,1,1,1,1,1,0,0,4,1,0,0,5
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octopus,0,0,1,0,0,1,1,0,0,0,0,0,8,0,0,1,7
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opossum,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,0,1
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oryx,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1
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ostrich,0,1,1,0,0,0,0,0,1,1,0,0,2,1,0,1,2
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parakeet,0,1,1,0,1,0,0,0,1,1,0,0,2,1,1,0,2
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penguin,0,1,1,0,0,1,1,0,1,1,0,0,2,1,0,1,2
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pheasant,0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,0,2
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pike,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,1,4
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piranha,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,4
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pitviper,0,0,1,0,0,0,1,1,1,1,1,0,0,1,0,0,3
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platypus,1,0,1,1,0,1,1,0,1,1,0,0,4,1,0,1,1
|
||||
polecat,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
|
||||
pony,1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,1,1
|
||||
porpoise,0,0,0,1,0,1,1,1,1,1,0,1,0,1,0,1,1
|
||||
puma,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
|
||||
pussycat,1,0,0,1,0,0,1,1,1,1,0,0,4,1,1,1,1
|
||||
raccoon,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
|
||||
reindeer,1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,1,1
|
||||
rhea,0,1,1,0,0,0,1,0,1,1,0,0,2,1,0,1,2
|
||||
scorpion,0,0,0,0,0,0,1,0,0,1,1,0,8,1,0,0,7
|
||||
seahorse,0,0,1,0,0,1,0,1,1,0,0,1,0,1,0,0,4
|
||||
seal,1,0,0,1,0,1,1,1,1,1,0,1,0,0,0,1,1
|
||||
sealion,1,0,0,1,0,1,1,1,1,1,0,1,2,1,0,1,1
|
||||
seasnake,0,0,0,0,0,1,1,1,1,0,1,0,0,1,0,0,3
|
||||
seawasp,0,0,1,0,0,1,1,0,0,0,1,0,0,0,0,0,7
|
||||
skimmer,0,1,1,0,1,1,1,0,1,1,0,0,2,1,0,0,2
|
||||
skua,0,1,1,0,1,1,1,0,1,1,0,0,2,1,0,0,2
|
||||
slowworm,0,0,1,0,0,0,1,1,1,1,0,0,0,1,0,0,3
|
||||
slug,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,7
|
||||
sole,0,0,1,0,0,1,0,1,1,0,0,1,0,1,0,0,4
|
||||
sparrow,0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,0,2
|
||||
squirrel,1,0,0,1,0,0,0,1,1,1,0,0,2,1,0,0,1
|
||||
starfish,0,0,1,0,0,1,1,0,0,0,0,0,5,0,0,0,7
|
||||
stingray,0,0,1,0,0,1,1,1,1,0,1,1,0,1,0,1,4
|
||||
swan,0,1,1,0,1,1,0,0,1,1,0,0,2,1,0,1,2
|
||||
termite,0,0,1,0,0,0,0,0,0,1,0,0,6,0,0,0,6
|
||||
toad,0,0,1,0,0,1,0,1,1,1,0,0,4,0,0,0,5
|
||||
tortoise,0,0,1,0,0,0,0,0,1,1,0,0,4,1,0,1,3
|
||||
tuatara,0,0,1,0,0,0,1,1,1,1,0,0,4,1,0,0,3
|
||||
tuna,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,1,4
|
||||
vampire,1,0,0,1,1,0,0,1,1,1,0,0,2,1,0,0,1
|
||||
vole,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,0,1
|
||||
vulture,0,1,1,0,1,0,1,0,1,1,0,0,2,1,0,1,2
|
||||
wallaby,1,0,0,1,0,0,0,1,1,1,0,0,2,1,0,1,1
|
||||
wasp,1,0,1,0,1,0,0,0,0,1,1,0,6,0,0,0,6
|
||||
wolf,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
|
||||
worm,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,7
|
||||
wren,0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,0,2
|
||||
|
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
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|
||||
digraph Tree {
|
||||
node [shape=box, style="filled, rounded", color="black", fontname=helvetica] ;
|
||||
edge [fontname=helvetica] ;
|
||||
0 [label="worst perimeter <= 106.05\ngini = 0.465\nsamples = 426\nvalue = [[269, 157]\n[157, 269]]", fillcolor="#e5813908"] ;
|
||||
1 [label="worst concave points <= 0.159\ngini = 0.067\nsamples = 259\nvalue = [[250, 9]\n[9, 250]]", fillcolor="#e58139db"] ;
|
||||
0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
|
||||
2 [label="worst concave points <= 0.135\ngini = 0.031\nsamples = 253\nvalue = [[249, 4]\n[4, 249]]", fillcolor="#e58139ee"] ;
|
||||
1 -> 2 ;
|
||||
3 [label="radius error <= 0.643\ngini = 0.008\nsamples = 242\nvalue = [[241, 1]\n[1, 241]]", fillcolor="#e58139fb"] ;
|
||||
2 -> 3 ;
|
||||
4 [label="gini = 0.0\nsamples = 239\nvalue = [[239, 0]\n[0, 239]]", fillcolor="#e58139ff"] ;
|
||||
3 -> 4 ;
|
||||
5 [label="worst symmetry <= 0.208\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#e5813913"] ;
|
||||
3 -> 5 ;
|
||||
6 [label="gini = 0.0\nsamples = 1\nvalue = [[0, 1]\n[1, 0]]", fillcolor="#e58139ff"] ;
|
||||
5 -> 6 ;
|
||||
7 [label="gini = 0.0\nsamples = 2\nvalue = [[2, 0]\n[0, 2]]", fillcolor="#e58139ff"] ;
|
||||
5 -> 7 ;
|
||||
8 [label="worst texture <= 29.455\ngini = 0.397\nsamples = 11\nvalue = [[8, 3]\n[3, 8]]", fillcolor="#e581392c"] ;
|
||||
2 -> 8 ;
|
||||
9 [label="gini = 0.0\nsamples = 8\nvalue = [[8, 0]\n[0, 8]]", fillcolor="#e58139ff"] ;
|
||||
8 -> 9 ;
|
||||
10 [label="gini = 0.0\nsamples = 3\nvalue = [[0, 3]\n[3, 0]]", fillcolor="#e58139ff"] ;
|
||||
8 -> 10 ;
|
||||
11 [label="mean texture <= 16.22\ngini = 0.278\nsamples = 6\nvalue = [[1, 5]\n[5, 1]]", fillcolor="#e581396b"] ;
|
||||
1 -> 11 ;
|
||||
12 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139ff"] ;
|
||||
11 -> 12 ;
|
||||
13 [label="gini = 0.0\nsamples = 5\nvalue = [[0, 5]\n[5, 0]]", fillcolor="#e58139ff"] ;
|
||||
11 -> 13 ;
|
||||
14 [label="worst texture <= 20.645\ngini = 0.202\nsamples = 167\nvalue = [[19, 148]\n[148, 19]]", fillcolor="#e5813994"] ;
|
||||
0 -> 14 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
|
||||
15 [label="worst radius <= 17.74\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#e5813938"] ;
|
||||
14 -> 15 ;
|
||||
16 [label="gini = 0.0\nsamples = 11\nvalue = [[11, 0]\n[0, 11]]", fillcolor="#e58139ff"] ;
|
||||
15 -> 16 ;
|
||||
17 [label="mean texture <= 13.745\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#e5813955"] ;
|
||||
15 -> 17 ;
|
||||
18 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139ff"] ;
|
||||
17 -> 18 ;
|
||||
19 [label="gini = 0.0\nsamples = 4\nvalue = [[0, 4]\n[4, 0]]", fillcolor="#e58139ff"] ;
|
||||
17 -> 19 ;
|
||||
20 [label="mean concave points <= 0.049\ngini = 0.088\nsamples = 151\nvalue = [[7, 144]\n[144, 7]]", fillcolor="#e58139d0"] ;
|
||||
14 -> 20 ;
|
||||
21 [label="concave points error <= 0.01\ngini = 0.48\nsamples = 15\nvalue = [[6, 9]\n[9, 6]]", fillcolor="#e5813900"] ;
|
||||
20 -> 21 ;
|
||||
22 [label="gini = 0.0\nsamples = 9\nvalue = [[0, 9]\n[9, 0]]", fillcolor="#e58139ff"] ;
|
||||
21 -> 22 ;
|
||||
23 [label="gini = 0.0\nsamples = 6\nvalue = [[6, 0]\n[0, 6]]", fillcolor="#e58139ff"] ;
|
||||
21 -> 23 ;
|
||||
24 [label="worst smoothness <= 0.096\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e58139f7"] ;
|
||||
20 -> 24 ;
|
||||
25 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139ff"] ;
|
||||
24 -> 25 ;
|
||||
26 [label="gini = 0.0\nsamples = 135\nvalue = [[0, 135]\n[135, 0]]", fillcolor="#e58139ff"] ;
|
||||
24 -> 26 ;
|
||||
}
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 216 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 80 KiB |
@@ -0,0 +1,11 @@
|
||||
Grade Trend,Hours slept,Hours Studied,Grade
|
||||
1,0,1,1
|
||||
0,1,0,0
|
||||
1,0,1,1
|
||||
1,1,1,1
|
||||
0,0,1,0
|
||||
1,0,0,0
|
||||
0,1,1,0
|
||||
0,0,1,0
|
||||
1,0,0,0
|
||||
1,1,1,1
|
||||
|
@@ -0,0 +1,11 @@
|
||||
Grade Trend,Hours slept,Hours Studied,Grade
|
||||
1 , 0 , 1 , 1
|
||||
0 , 1 , 0 , 0
|
||||
1 , 0 , 1 , 1
|
||||
1 , 1 , 1 , 1
|
||||
0 , 0 , 1 , 0
|
||||
1 , 0 , 0 , 0
|
||||
0 , 1 , 1 , 0
|
||||
0 , 0 , 1 , 0
|
||||
1 , 0 , 0 , 0
|
||||
1 , 1 , 1 , 1
|
||||
@@ -0,0 +1,15 @@
|
||||
Outlook,Temperature,Humidity,Wind,Ride
|
||||
0,0,0,0,0
|
||||
0,0,0,1,1
|
||||
1,0,0,0,1
|
||||
2,1,0,0,1
|
||||
2,2,1,0,1
|
||||
2,2,1,1,0
|
||||
1,2,1,1,1
|
||||
0,1,0,0,0
|
||||
0,2,1,0,1
|
||||
2,1,1,0,1
|
||||
0,1,1,1,1
|
||||
1,1,0,1,1
|
||||
1,0,1,0,1
|
||||
2,1,0,1,0
|
||||
|
@@ -0,0 +1,15 @@
|
||||
Outlook,Temperature,Humidity,Wind,Ride
|
||||
Sunny,Hot,High,Weak,0
|
||||
Sunny,Hot,High,Strong,1
|
||||
Overcast,Hot,High,Weak,1
|
||||
Rain,Mild,High,Weak,1
|
||||
Rain,Cool,Normal,Weak,1
|
||||
Rain,Cool,Normal,Strong,0
|
||||
Overcast,Cool,Normal,Strong,1
|
||||
Sunny,Mild,High,Weak,0
|
||||
Sunny,Cool,Normal,Weak,1
|
||||
Rain,Mild,Normal,Weak,1
|
||||
Sunny,Mild,Normal,Strong,1
|
||||
Overcast,Mild,High,Strong,1
|
||||
Overcast,Hot,Normal,Weak,1
|
||||
Rain,Mild,High,Strong,0
|
||||
@@ -0,0 +1,13 @@
|
||||
digraph Tree {
|
||||
node [shape=box, style="filled, rounded", color="black", fontname=helvetica] ;
|
||||
edge [fontname=helvetica] ;
|
||||
0 [label="X[7] <= 0.5\ngini = 0.48\nsamples = 15\nvalue = [4, 10, 1]", fillcolor="#39e5818b"] ;
|
||||
1 [label="X[1] <= 0.5\ngini = 0.408\nsamples = 14\nvalue = [4, 10, 0]", fillcolor="#39e58199"] ;
|
||||
0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
|
||||
2 [label="gini = 0.48\nsamples = 10\nvalue = [4, 6, 0]", fillcolor="#39e58155"] ;
|
||||
1 -> 2 ;
|
||||
3 [label="gini = 0.0\nsamples = 4\nvalue = [0, 4, 0]", fillcolor="#39e581ff"] ;
|
||||
1 -> 3 ;
|
||||
4 [label="gini = 0.0\nsamples = 1\nvalue = [0, 0, 1]", fillcolor="#8139e5ff"] ;
|
||||
0 -> 4 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
|
||||
}
|
||||
@@ -0,0 +1,15 @@
|
||||
Day,Outlook,Temperature,Humidity,Wind,Ride
|
||||
1,Sunny,Hot,High,Weak,0
|
||||
2,Sunny,Hot,High,Strong,1
|
||||
3,Overcast,Hot,High,Weak,1
|
||||
4,Rain,Mild,High,Weak,1
|
||||
5,Rain,Cool,Normal,Weak,1
|
||||
6,Rain,Cool,Normal,Strong,0
|
||||
7,Overcast,Cool,Normal,Strong,1
|
||||
8,Sunny,Mild,High,Weak,0
|
||||
9,Sunny,Cool,Normal,Weak,1
|
||||
10,Rain,Mild,Normal,Weak,1
|
||||
11,Sunny,Mild,Normal,Strong,1
|
||||
12,Overcast,Mild,High,Strong,1
|
||||
13,Overcast,Hot,Normal,Weak,1
|
||||
14,Rain,Mild,High,Strong,0
|
||||
|
@@ -0,0 +1,101 @@
|
||||
aardvark,1,0,0,1,0,0,1,1,1,1,0,0,4,0,0,1,1
|
||||
antelope,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1
|
||||
bass,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,4
|
||||
bear,1,0,0,1,0,0,1,1,1,1,0,0,4,0,0,1,1
|
||||
boar,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
|
||||
buffalo,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1
|
||||
calf,1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,1,1
|
||||
carp,0,0,1,0,0,1,0,1,1,0,0,1,0,1,1,0,4
|
||||
catfish,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,4
|
||||
cavy,1,0,0,1,0,0,0,1,1,1,0,0,4,0,1,0,1
|
||||
cheetah,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
|
||||
chicken,0,1,1,0,1,0,0,0,1,1,0,0,2,1,1,0,2
|
||||
chub,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,4
|
||||
clam,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,7
|
||||
crab,0,0,1,0,0,1,1,0,0,0,0,0,4,0,0,0,7
|
||||
crayfish,0,0,1,0,0,1,1,0,0,0,0,0,6,0,0,0,7
|
||||
crow,0,1,1,0,1,0,1,0,1,1,0,0,2,1,0,0,2
|
||||
deer,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1
|
||||
dogfish,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,1,4
|
||||
dolphin,0,0,0,1,0,1,1,1,1,1,0,1,0,1,0,1,1
|
||||
dove,0,1,1,0,1,0,0,0,1,1,0,0,2,1,1,0,2
|
||||
duck,0,1,1,0,1,1,0,0,1,1,0,0,2,1,0,0,2
|
||||
elephant,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1
|
||||
flamingo,0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,1,2
|
||||
flea,0,0,1,0,0,0,0,0,0,1,0,0,6,0,0,0,6
|
||||
frog,0,0,1,0,0,1,1,1,1,1,0,0,4,0,0,0,5
|
||||
frog,0,0,1,0,0,1,1,1,1,1,1,0,4,0,0,0,5
|
||||
fruitbat,1,0,0,1,1,0,0,1,1,1,0,0,2,1,0,0,1
|
||||
giraffe,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1
|
||||
girl,1,0,0,1,0,0,1,1,1,1,0,0,2,0,1,1,1
|
||||
gnat,0,0,1,0,1,0,0,0,0,1,0,0,6,0,0,0,6
|
||||
goat,1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,1,1
|
||||
gorilla,1,0,0,1,0,0,0,1,1,1,0,0,2,0,0,1,1
|
||||
gull,0,1,1,0,1,1,1,0,1,1,0,0,2,1,0,0,2
|
||||
haddock,0,0,1,0,0,1,0,1,1,0,0,1,0,1,0,0,4
|
||||
hamster,1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,0,1
|
||||
hare,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,0,1
|
||||
hawk,0,1,1,0,1,0,1,0,1,1,0,0,2,1,0,0,2
|
||||
herring,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,4
|
||||
honeybee,1,0,1,0,1,0,0,0,0,1,1,0,6,0,1,0,6
|
||||
housefly,1,0,1,0,1,0,0,0,0,1,0,0,6,0,0,0,6
|
||||
kiwi,0,1,1,0,0,0,1,0,1,1,0,0,2,1,0,0,2
|
||||
ladybird,0,0,1,0,1,0,1,0,0,1,0,0,6,0,0,0,6
|
||||
lark,0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,0,2
|
||||
leopard,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
|
||||
lion,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
|
||||
lobster,0,0,1,0,0,1,1,0,0,0,0,0,6,0,0,0,7
|
||||
lynx,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
|
||||
mink,1,0,0,1,0,1,1,1,1,1,0,0,4,1,0,1,1
|
||||
mole,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,0,1
|
||||
mongoose,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
|
||||
moth,1,0,1,0,1,0,0,0,0,1,0,0,6,0,0,0,6
|
||||
newt,0,0,1,0,0,1,1,1,1,1,0,0,4,1,0,0,5
|
||||
octopus,0,0,1,0,0,1,1,0,0,0,0,0,8,0,0,1,7
|
||||
opossum,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,0,1
|
||||
oryx,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1
|
||||
ostrich,0,1,1,0,0,0,0,0,1,1,0,0,2,1,0,1,2
|
||||
parakeet,0,1,1,0,1,0,0,0,1,1,0,0,2,1,1,0,2
|
||||
penguin,0,1,1,0,0,1,1,0,1,1,0,0,2,1,0,1,2
|
||||
pheasant,0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,0,2
|
||||
pike,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,1,4
|
||||
piranha,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,4
|
||||
pitviper,0,0,1,0,0,0,1,1,1,1,1,0,0,1,0,0,3
|
||||
platypus,1,0,1,1,0,1,1,0,1,1,0,0,4,1,0,1,1
|
||||
polecat,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
|
||||
pony,1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,1,1
|
||||
porpoise,0,0,0,1,0,1,1,1,1,1,0,1,0,1,0,1,1
|
||||
puma,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
|
||||
pussycat,1,0,0,1,0,0,1,1,1,1,0,0,4,1,1,1,1
|
||||
raccoon,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
|
||||
reindeer,1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,1,1
|
||||
rhea,0,1,1,0,0,0,1,0,1,1,0,0,2,1,0,1,2
|
||||
scorpion,0,0,0,0,0,0,1,0,0,1,1,0,8,1,0,0,7
|
||||
seahorse,0,0,1,0,0,1,0,1,1,0,0,1,0,1,0,0,4
|
||||
seal,1,0,0,1,0,1,1,1,1,1,0,1,0,0,0,1,1
|
||||
sealion,1,0,0,1,0,1,1,1,1,1,0,1,2,1,0,1,1
|
||||
seasnake,0,0,0,0,0,1,1,1,1,0,1,0,0,1,0,0,3
|
||||
seawasp,0,0,1,0,0,1,1,0,0,0,1,0,0,0,0,0,7
|
||||
skimmer,0,1,1,0,1,1,1,0,1,1,0,0,2,1,0,0,2
|
||||
skua,0,1,1,0,1,1,1,0,1,1,0,0,2,1,0,0,2
|
||||
slowworm,0,0,1,0,0,0,1,1,1,1,0,0,0,1,0,0,3
|
||||
slug,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,7
|
||||
sole,0,0,1,0,0,1,0,1,1,0,0,1,0,1,0,0,4
|
||||
sparrow,0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,0,2
|
||||
squirrel,1,0,0,1,0,0,0,1,1,1,0,0,2,1,0,0,1
|
||||
starfish,0,0,1,0,0,1,1,0,0,0,0,0,5,0,0,0,7
|
||||
stingray,0,0,1,0,0,1,1,1,1,0,1,1,0,1,0,1,4
|
||||
swan,0,1,1,0,1,1,0,0,1,1,0,0,2,1,0,1,2
|
||||
termite,0,0,1,0,0,0,0,0,0,1,0,0,6,0,0,0,6
|
||||
toad,0,0,1,0,0,1,0,1,1,1,0,0,4,0,0,0,5
|
||||
tortoise,0,0,1,0,0,0,0,0,1,1,0,0,4,1,0,1,3
|
||||
tuatara,0,0,1,0,0,0,1,1,1,1,0,0,4,1,0,0,3
|
||||
tuna,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,1,4
|
||||
vampire,1,0,0,1,1,0,0,1,1,1,0,0,2,1,0,0,1
|
||||
vole,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,0,1
|
||||
vulture,0,1,1,0,1,0,1,0,1,1,0,0,2,1,0,1,2
|
||||
wallaby,1,0,0,1,0,0,0,1,1,1,0,0,2,1,0,1,1
|
||||
wasp,1,0,1,0,1,0,0,0,0,1,1,0,6,0,0,0,6
|
||||
wolf,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
|
||||
worm,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,7
|
||||
wren,0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,0,2
|
||||
|
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Load Diff
Reference in New Issue
Block a user