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Morten Hjorth-Jensen
2024-11-24 10:34:47 +01:00
parent 64b9af7c9d
commit d63cb194d5
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@@ -37,7 +37,11 @@ doconce format html week48.do.txt --html_style=bootstrap --pygments_html_style=d
<!-- tocinfo
{'highest level': 2,
'sections': [('Overview of week 48', 2, None, 'overview-of-week-48'),
('Plan for week 47', 2, None, 'plan-for-week-47'),
('Lecture Monday, November 25',
2,
None,
'lecture-monday-november-25'),
('Lab sessions', 2, None, 'lab-sessions'),
('Random Forest Algorithm, reminder from last week',
2,
None,
@@ -277,77 +281,78 @@ MathJax.Hub.Config({
<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
<ul class="dropdown-menu">
<!-- navigation toc: --> <li><a href="._week48-bs001.html#overview-of-week-48" style="font-size: 80%;">Overview of week 48</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs002.html#plan-for-week-47" style="font-size: 80%;">Plan for week 47</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs003.html#random-forest-algorithm-reminder-from-last-week" style="font-size: 80%;">Random Forest Algorithm, reminder from last week</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs004.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="._week48-bs005.html#compare-bagging-on-trees-with-random-forests" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs006.html#boosting-a-bird-s-eye-view" style="font-size: 80%;">Boosting, a Bird's Eye View</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs007.html#what-is-boosting-additive-modelling-iterative-fitting" style="font-size: 80%;">What is boosting? Additive Modelling/Iterative Fitting</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs008.html#iterative-fitting-regression-and-squared-error-cost-function" style="font-size: 80%;">Iterative Fitting, Regression and Squared-error Cost Function</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs009.html#squared-error-example-and-iterative-fitting" style="font-size: 80%;">Squared-Error Example and Iterative Fitting</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs010.html#iterative-fitting-classification-and-adaboost" style="font-size: 80%;">Iterative Fitting, Classification and AdaBoost</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs011.html#adaptive-boosting-adaboost" style="font-size: 80%;">Adaptive Boosting, AdaBoost</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs012.html#building-up-adaboost" style="font-size: 80%;">Building up AdaBoost</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs013.html#adaptive-boosting-adaboost-basic-algorithm" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs014.html#basic-steps-of-adaboost" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs015.html#adaboost-examples" style="font-size: 80%;">AdaBoost Examples</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs016.html#making-an-adaboost-code-yourself" style="font-size: 80%;">Making an ADAboost code yourself</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs017.html#gradient-boosting-basics-with-steepest-descent-functional-gradient-descent" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs018.html#the-squared-error-again-steepest-descent" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs019.html#steepest-descent-example" style="font-size: 80%;">Steepest Descent Example</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs020.html#gradient-boosting-algorithm" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs021.html#gradient-boosting-examples-of-regression" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs022.html#gradient-boosting-classification-example" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs023.html#xgboost-extreme-gradient-boosting" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs024.html#xgboost-on-the-cancer-data" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs025.html#gradient-boosting-making-our-own-code-for-a-regression-case" style="font-size: 80%;">Gradient boosting, making our own code for a regression case</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs026.html#summary-of-course" style="font-size: 80%;">Summary of course</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs027.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="._week48-bs028.html#topics-we-have-covered-this-year" style="font-size: 80%;">Topics we have covered this year</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs029.html#statistical-analysis-and-optimization-of-data" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs030.html#machine-learning" style="font-size: 80%;">Machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs031.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="._week48-bs032.html#perspective-on-machine-learning" style="font-size: 80%;">Perspective on Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs033.html#machine-learning-research" style="font-size: 80%;">Machine Learning Research</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs034.html#starting-your-machine-learning-project" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs035.html#choose-a-model-and-algorithm" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs036.html#preparing-your-data" style="font-size: 80%;">Preparing Your Data</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs037.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="._week48-bs038.html#optimization-methods-and-hyperparameters" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs039.html#resampling" style="font-size: 80%;">Resampling</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs040.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="._week48-bs041.html#additional-courses-of-interest" style="font-size: 80%;">Additional courses of interest</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs042.html#what-s-the-future-like" style="font-size: 80%;">What's the future like?</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs043.html#types-of-machine-learning-a-repetition" style="font-size: 80%;">Types of Machine Learning, a repetition</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs044.html#why-boltzmann-machines" style="font-size: 80%;">Why Boltzmann machines?</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs045.html#boltzmann-machines" style="font-size: 80%;">Boltzmann Machines</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs046.html#some-similarities-and-differences-from-dnns" style="font-size: 80%;">Some similarities and differences from DNNs</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs047.html#boltzmann-machines-bm" style="font-size: 80%;">Boltzmann machines (BM)</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs048.html#a-standard-bm-setup" style="font-size: 80%;">A standard BM setup</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs049.html#the-structure-of-the-rbm-network" style="font-size: 80%;">The structure of the RBM network</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs050.html#the-network" style="font-size: 80%;">The network</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs051.html#goals" style="font-size: 80%;">Goals</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs052.html#joint-distribution" style="font-size: 80%;">Joint distribution</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs053.html#network-elements-the-energy-function" style="font-size: 80%;">Network Elements, the energy function</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs054.html#defining-different-types-of-rbms" style="font-size: 80%;">Defining different types of RBMs</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs055.html#more-about-rbms" style="font-size: 80%;">More about RBMs</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs056.html#autoencoders-overarching-view" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs057.html#bayesian-machine-learning" style="font-size: 80%;">Bayesian Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs058.html#reinforcement-learning" style="font-size: 80%;">Reinforcement Learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs059.html#transfer-learning" style="font-size: 80%;">Transfer learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs060.html#adversarial-learning" style="font-size: 80%;">Adversarial learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs061.html#dual-learning" style="font-size: 80%;">Dual learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs062.html#distributed-machine-learning" style="font-size: 80%;">Distributed machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs063.html#meta-learning" style="font-size: 80%;">Meta learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs064.html#the-challenges-facing-machine-learning" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs065.html#explainable-machine-learning" style="font-size: 80%;">Explainable machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs066.html#quantum-machine-learning" style="font-size: 80%;">Quantum machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs067.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="._week48-bs068.html#quantum-reinforcement-learning" style="font-size: 80%;">Quantum reinforcement learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs069.html#quantum-deep-learning" style="font-size: 80%;">Quantum deep learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs070.html#social-machine-learning" style="font-size: 80%;">Social machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs071.html#the-last-words" style="font-size: 80%;">The last words?</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs072.html#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>
<!-- navigation toc: --> <li><a href="._week48-bs002.html#lecture-monday-november-25" style="font-size: 80%;">Lecture Monday, November 25</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs003.html#lab-sessions" style="font-size: 80%;">Lab sessions</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs004.html#random-forest-algorithm-reminder-from-last-week" style="font-size: 80%;">Random Forest Algorithm, reminder from last week</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs005.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="._week48-bs006.html#compare-bagging-on-trees-with-random-forests" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs007.html#boosting-a-bird-s-eye-view" style="font-size: 80%;">Boosting, a Bird's Eye View</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs008.html#what-is-boosting-additive-modelling-iterative-fitting" style="font-size: 80%;">What is boosting? Additive Modelling/Iterative Fitting</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs009.html#iterative-fitting-regression-and-squared-error-cost-function" style="font-size: 80%;">Iterative Fitting, Regression and Squared-error Cost Function</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs010.html#squared-error-example-and-iterative-fitting" style="font-size: 80%;">Squared-Error Example and Iterative Fitting</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs011.html#iterative-fitting-classification-and-adaboost" style="font-size: 80%;">Iterative Fitting, Classification and AdaBoost</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs012.html#adaptive-boosting-adaboost" style="font-size: 80%;">Adaptive Boosting, AdaBoost</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs013.html#building-up-adaboost" style="font-size: 80%;">Building up AdaBoost</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs014.html#adaptive-boosting-adaboost-basic-algorithm" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs015.html#basic-steps-of-adaboost" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs016.html#adaboost-examples" style="font-size: 80%;">AdaBoost Examples</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs017.html#making-an-adaboost-code-yourself" style="font-size: 80%;">Making an ADAboost code yourself</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs018.html#gradient-boosting-basics-with-steepest-descent-functional-gradient-descent" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs019.html#the-squared-error-again-steepest-descent" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs020.html#steepest-descent-example" style="font-size: 80%;">Steepest Descent Example</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs021.html#gradient-boosting-algorithm" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs022.html#gradient-boosting-examples-of-regression" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs023.html#gradient-boosting-classification-example" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs024.html#xgboost-extreme-gradient-boosting" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs025.html#xgboost-on-the-cancer-data" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs026.html#gradient-boosting-making-our-own-code-for-a-regression-case" style="font-size: 80%;">Gradient boosting, making our own code for a regression case</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs027.html#summary-of-course" style="font-size: 80%;">Summary of course</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs028.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="._week48-bs029.html#topics-we-have-covered-this-year" style="font-size: 80%;">Topics we have covered this year</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs030.html#statistical-analysis-and-optimization-of-data" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs031.html#machine-learning" style="font-size: 80%;">Machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs032.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="._week48-bs033.html#perspective-on-machine-learning" style="font-size: 80%;">Perspective on Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs034.html#machine-learning-research" style="font-size: 80%;">Machine Learning Research</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs035.html#starting-your-machine-learning-project" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs036.html#choose-a-model-and-algorithm" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs037.html#preparing-your-data" style="font-size: 80%;">Preparing Your Data</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs038.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="._week48-bs039.html#optimization-methods-and-hyperparameters" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs040.html#resampling" style="font-size: 80%;">Resampling</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs041.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="._week48-bs042.html#additional-courses-of-interest" style="font-size: 80%;">Additional courses of interest</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs043.html#what-s-the-future-like" style="font-size: 80%;">What's the future like?</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs044.html#types-of-machine-learning-a-repetition" style="font-size: 80%;">Types of Machine Learning, a repetition</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs045.html#why-boltzmann-machines" style="font-size: 80%;">Why Boltzmann machines?</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs046.html#boltzmann-machines" style="font-size: 80%;">Boltzmann Machines</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs047.html#some-similarities-and-differences-from-dnns" style="font-size: 80%;">Some similarities and differences from DNNs</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs048.html#boltzmann-machines-bm" style="font-size: 80%;">Boltzmann machines (BM)</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs049.html#a-standard-bm-setup" style="font-size: 80%;">A standard BM setup</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs050.html#the-structure-of-the-rbm-network" style="font-size: 80%;">The structure of the RBM network</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs051.html#the-network" style="font-size: 80%;">The network</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs052.html#goals" style="font-size: 80%;">Goals</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs053.html#joint-distribution" style="font-size: 80%;">Joint distribution</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs054.html#network-elements-the-energy-function" style="font-size: 80%;">Network Elements, the energy function</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs055.html#defining-different-types-of-rbms" style="font-size: 80%;">Defining different types of RBMs</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs056.html#more-about-rbms" style="font-size: 80%;">More about RBMs</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs057.html#autoencoders-overarching-view" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs058.html#bayesian-machine-learning" style="font-size: 80%;">Bayesian Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs059.html#reinforcement-learning" style="font-size: 80%;">Reinforcement Learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs060.html#transfer-learning" style="font-size: 80%;">Transfer learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs061.html#adversarial-learning" style="font-size: 80%;">Adversarial learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs062.html#dual-learning" style="font-size: 80%;">Dual learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs063.html#distributed-machine-learning" style="font-size: 80%;">Distributed machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs064.html#meta-learning" style="font-size: 80%;">Meta learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs065.html#the-challenges-facing-machine-learning" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs066.html#explainable-machine-learning" style="font-size: 80%;">Explainable machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs067.html#quantum-machine-learning" style="font-size: 80%;">Quantum machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs068.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="._week48-bs069.html#quantum-reinforcement-learning" style="font-size: 80%;">Quantum reinforcement learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs070.html#quantum-deep-learning" style="font-size: 80%;">Quantum deep learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs071.html#social-machine-learning" style="font-size: 80%;">Social machine learning</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs072.html#the-last-words" style="font-size: 80%;">The last words?</a></li>
<!-- navigation toc: --> <li><a href="._week48-bs073.html#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>
@@ -374,7 +379,7 @@ MathJax.Hub.Config({
</center>
<br>
<center>
<h4>Nov 23, 2024</h4>
<h4>Nov 24, 2024</h4>
</center> <!-- date -->
<br>
@@ -399,7 +404,7 @@ MathJax.Hub.Config({
<li><a href="._week48-bs008.html">9</a></li>
<li><a href="._week48-bs009.html">10</a></li>
<li><a href="">...</a></li>
<li><a href="._week48-bs072.html">73</a></li>
<li><a href="._week48-bs073.html">74</a></li>
<li><a href="._week48-bs001.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
doc/pub/week48/html/week48-reveal.html
+20 -19
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@@ -181,7 +181,7 @@ MathJax.Hub.Config({
</center>
<br>
<center>
<h4>Nov 23, 2024</h4>
<h4>Nov 24, 2024</h4>
</center> <!-- date -->
<br>
@@ -196,24 +196,7 @@ MathJax.Hub.Config({
</section>
<section>
<h2 id="plan-for-week-47">Plan for week 47 </h2>
<div class="alert alert-block alert-block alert-text-normal">
<b>Lab sessions on Tuesday and Wednesday</b>
<p>
<ul>
<p><li> Work and Discussion of project 3</li>
<p><li> Last weekly exercise</li>
<p><li> Lab sessions at usual times.</li>
<p><li> For the week of December 2-6, lab sessions start at 10am and end at 4pm, room F&#216;434, Tuesday and Wednesday</li>
</ul>
</div>
<h2 id="lecture-monday-november-25">Lecture Monday, November 25 </h2>
<div class="alert alert-block alert-block alert-text-normal">
<b>Plans for the lecture Monday 25 November, with video suggestions etc</b>
<p>
@@ -237,6 +220,24 @@ MathJax.Hub.Config({
</div>
</section>
<section>
<h2 id="lab-sessions">Lab sessions </h2>
<div class="alert alert-block alert-block alert-text-normal">
<b>Lab sessions on Tuesday and Wednesday</b>
<p>
<ul>
<p><li> Work and Discussion of project 3</li>
<p><li> Last weekly exercise</li>
<p><li> Lab sessions at usual times.</li>
<p><li> For the week of December 2-6, lab sessions start at 10am and end at 4pm, room F&#216;434, Tuesday and Wednesday</li>
</ul>
</div>
</section>
<section>
<h2 id="random-forest-algorithm-reminder-from-last-week">Random Forest Algorithm, reminder from last week </h2>
doc/pub/week48/html/week48-solarized.html
+21 -16
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@@ -64,7 +64,11 @@ div.toc p,a {
<!-- tocinfo
{'highest level': 2,
'sections': [('Overview of week 48', 2, None, 'overview-of-week-48'),
('Plan for week 47', 2, None, 'plan-for-week-47'),
('Lecture Monday, November 25',
2,
None,
'lecture-monday-november-25'),
('Lab sessions', 2, None, 'lab-sessions'),
('Random Forest Algorithm, reminder from last week',
2,
None,
@@ -303,7 +307,7 @@ MathJax.Hub.Config({
</center>
<br>
<center>
<h4>Nov 23, 2024</h4>
<h4>Nov 24, 2024</h4>
</center> <!-- date -->
<br>
@@ -311,20 +315,7 @@ MathJax.Hub.Config({
<h2 id="overview-of-week-48">Overview of week 48 </h2>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="plan-for-week-47">Plan for week 47 </h2>
<div class="alert alert-block alert-block alert-text-normal">
<b>Lab sessions on Tuesday and Wednesday</b>
<p>
<ul>
<li> Work and Discussion of project 3</li>
<li> Last weekly exercise</li>
<li> Lab sessions at usual times.</li>
<li> For the week of December 2-6, lab sessions start at 10am and end at 4pm, room F&#216;434, Tuesday and Wednesday</li>
</ul>
</div>
<h2 id="lecture-monday-november-25">Lecture Monday, November 25 </h2>
<div class="alert alert-block alert-block alert-text-normal">
<b>Plans for the lecture Monday 25 November, with video suggestions etc</b>
<p>
@@ -347,6 +338,20 @@ MathJax.Hub.Config({
</div>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="lab-sessions">Lab sessions </h2>
<div class="alert alert-block alert-block alert-text-normal">
<b>Lab sessions on Tuesday and Wednesday</b>
<p>
<ul>
<li> Work and Discussion of project 3</li>
<li> Last weekly exercise</li>
<li> Lab sessions at usual times.</li>
<li> For the week of December 2-6, lab sessions start at 10am and end at 4pm, room F&#216;434, Tuesday and Wednesday</li>
</ul>
</div>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="random-forest-algorithm-reminder-from-last-week">Random Forest Algorithm, reminder from last week </h2>
doc/pub/week48/html/week48.html
+21 -16
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@@ -141,7 +141,11 @@ div.toc p,a {
<!-- tocinfo
{'highest level': 2,
'sections': [('Overview of week 48', 2, None, 'overview-of-week-48'),
('Plan for week 47', 2, None, 'plan-for-week-47'),
('Lecture Monday, November 25',
2,
None,
'lecture-monday-november-25'),
('Lab sessions', 2, None, 'lab-sessions'),
('Random Forest Algorithm, reminder from last week',
2,
None,
@@ -380,7 +384,7 @@ MathJax.Hub.Config({
</center>
<br>
<center>
<h4>Nov 23, 2024</h4>
<h4>Nov 24, 2024</h4>
</center> <!-- date -->
<br>
@@ -388,20 +392,7 @@ MathJax.Hub.Config({
<h2 id="overview-of-week-48">Overview of week 48 </h2>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="plan-for-week-47">Plan for week 47 </h2>
<div class="alert alert-block alert-block alert-text-normal">
<b>Lab sessions on Tuesday and Wednesday</b>
<p>
<ul>
<li> Work and Discussion of project 3</li>
<li> Last weekly exercise</li>
<li> Lab sessions at usual times.</li>
<li> For the week of December 2-6, lab sessions start at 10am and end at 4pm, room F&#216;434, Tuesday and Wednesday</li>
</ul>
</div>
<h2 id="lecture-monday-november-25">Lecture Monday, November 25 </h2>
<div class="alert alert-block alert-block alert-text-normal">
<b>Plans for the lecture Monday 25 November, with video suggestions etc</b>
<p>
@@ -424,6 +415,20 @@ MathJax.Hub.Config({
</div>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="lab-sessions">Lab sessions </h2>
<div class="alert alert-block alert-block alert-text-normal">
<b>Lab sessions on Tuesday and Wednesday</b>
<p>
<ul>
<li> Work and Discussion of project 3</li>
<li> Last weekly exercise</li>
<li> Lab sessions at usual times.</li>
<li> For the week of December 2-6, lab sessions start at 10am and end at 4pm, room F&#216;434, Tuesday and Wednesday</li>
</ul>
</div>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="random-forest-algorithm-reminder-from-last-week">Random Forest Algorithm, reminder from last week </h2>
doc/pub/week48/ipynb/ipynb-week48-src.tar.gz
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doc/pub/week48/ipynb/week48.ipynb
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doc/src/week48/week48.do.txt
+12 -9
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@@ -5,16 +5,9 @@ DATE: today
!split
===== Overview of week 48 =====
!split
===== Plan for week 47 =====
!bblock Lab sessions on Tuesday and Wednesday
* Work and Discussion of project 3
* Last weekly exercise
* Lab sessions at usual times.
* For the week of December 2-6, lab sessions start at 10am and end at 4pm, room FØ434, Tuesday and Wednesday
!eblock
===== Lecture Monday, November 25 =====
!bblock Plans for the lecture Monday 25 November, with video suggestions etc
o Boosting and gradient boosting and ensemble models
o Summary of course
@@ -31,6 +24,16 @@ o Readings and Videos:
!eblock
!split
===== Lab sessions =====
!bblock Lab sessions on Tuesday and Wednesday
* Work and Discussion of project 3
* Last weekly exercise
* Lab sessions at usual times.
* For the week of December 2-6, lab sessions start at 10am and end at 4pm, room FØ434, Tuesday and Wednesday
!eblock
!split
===== Random Forest Algorithm, reminder from last week =====