Morten Hjorth-Jensen
426 lines
25 KiB
HTML
426 lines
25 KiB
HTML
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<!-- tocinfo
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{'highest level': 2,
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'sections': [('Overview of week 48', 2, None, 'overview-of-week-48'),
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('Lecture Monday, November 25',
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2,
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None,
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'lecture-monday-november-25'),
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('Lab sessions', 2, None, 'lab-sessions'),
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('Random Forest Algorithm, reminder from last week',
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2,
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None,
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|
'random-forest-algorithm-reminder-from-last-week'),
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('Random Forests Compared with other Methods on the Cancer Data',
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2,
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None,
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('Compare Bagging on Trees with Random Forests',
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'compare-bagging-on-trees-with-random-forests'),
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("Boosting, a Bird's Eye View",
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2,
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None,
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'boosting-a-bird-s-eye-view'),
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('What is boosting? Additive Modelling/Iterative Fitting',
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('Iterative Fitting, Regression and Squared-error Cost Function',
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('Adaptive Boosting, AdaBoost',
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('Building up AdaBoost', 2, None, 'building-up-adaboost'),
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('Adaptive boosting: AdaBoost, Basic Algorithm',
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('Basic Steps of AdaBoost', 2, None, 'basic-steps-of-adaboost'),
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('AdaBoost Examples', 2, None, 'adaboost-examples'),
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('Making an ADAboost code yourself',
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2,
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None,
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('Gradient boosting: Basics with Steepest Descent/Functional '
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'Gradient Descent',
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2,
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None,
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'gradient-boosting-basics-with-steepest-descent-functional-gradient-descent'),
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('The Squared-Error again! Steepest Descent',
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'the-squared-error-again-steepest-descent'),
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('Steepest Descent Example', 2, None, 'steepest-descent-example'),
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('Gradient Boosting, algorithm',
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2,
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None,
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('Gradient Boosting, Examples of Regression',
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2,
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('Gradient Boosting, Classification Example',
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2,
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None,
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('XGBoost: Extreme Gradient Boosting',
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2,
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None,
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'xgboost-extreme-gradient-boosting'),
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('Xgboost on the Cancer Data',
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2,
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None,
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('Gradient boosting, making our own code for a regression case',
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2,
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None,
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'gradient-boosting-making-our-own-code-for-a-regression-case'),
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('Summary of course', 2, None, 'summary-of-course'),
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('What? Me worry? No final exam in this course!',
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2,
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None,
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('Topics we have covered this year',
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2,
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('Statistical analysis and optimization of data',
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2,
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('Machine learning', 2, None, 'machine-learning'),
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('Learning outcomes and overarching aims of this course',
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2,
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('Perspective on Machine Learning',
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('Machine Learning Research',
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2,
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('Starting your Machine Learning Project',
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2,
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('Choose a Model and Algorithm',
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2,
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None,
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'choose-a-model-and-algorithm'),
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('Preparing Your Data', 2, None, 'preparing-your-data'),
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('Which activation and weights to choose in neural networks',
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2,
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None,
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'which-activation-and-weights-to-choose-in-neural-networks'),
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('Optimization Methods and Hyperparameters',
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2,
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None,
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'optimization-methods-and-hyperparameters'),
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('Resampling', 2, None, 'resampling'),
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('Other courses on Data science and Machine Learning at UiO',
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2,
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None,
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'other-courses-on-data-science-and-machine-learning-at-uio'),
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('Additional courses of interest',
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2,
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None,
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'additional-courses-of-interest'),
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("What's the future like?", 2, None, 'what-s-the-future-like'),
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('Types of Machine Learning, a repetition',
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2,
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None,
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'types-of-machine-learning-a-repetition'),
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('Why Boltzmann machines?', 2, None, 'why-boltzmann-machines'),
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('Boltzmann Machines', 2, None, 'boltzmann-machines'),
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('Some similarities and differences from DNNs',
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2,
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None,
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('Boltzmann machines (BM)', 2, None, 'boltzmann-machines-bm'),
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('A standard BM setup', 2, None, 'a-standard-bm-setup'),
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('The structure of the RBM network',
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None,
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('The network', 2, None, 'the-network'),
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('Goals', 2, None, 'goals'),
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('Joint distribution', 2, None, 'joint-distribution'),
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('Network Elements, the energy function',
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2,
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None,
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('Defining different types of RBMs',
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2,
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None,
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'defining-different-types-of-rbms'),
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('More about RBMs', 2, None, 'more-about-rbms'),
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('Autoencoders: Overarching view',
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2,
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None,
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('Bayesian Machine Learning',
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('Reinforcement Learning', 2, None, 'reinforcement-learning'),
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('Transfer learning', 2, None, 'transfer-learning'),
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('Adversarial learning', 2, None, 'adversarial-learning'),
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('Dual learning', 2, None, 'dual-learning'),
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('Distributed machine learning',
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2,
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None,
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'distributed-machine-learning'),
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('Meta learning', 2, None, 'meta-learning'),
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('The Challenges Facing Machine Learning',
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2,
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|
None,
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('Explainable machine learning',
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2,
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|
None,
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'explainable-machine-learning'),
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|
('Quantum machine learning', 2, None, 'quantum-machine-learning'),
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('Quantum machine learning algorithms based on linear algebra',
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|
2,
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|
None,
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|
'quantum-machine-learning-algorithms-based-on-linear-algebra'),
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('Quantum reinforcement learning',
|
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2,
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|
None,
|
|
'quantum-reinforcement-learning'),
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|
('Quantum deep learning', 2, None, 'quantum-deep-learning'),
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|
('Social machine learning', 2, None, 'social-machine-learning'),
|
|
('The last words?', 2, None, 'the-last-words'),
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|
('Best wishes to you all and thanks so much for your heroic '
|
|
'efforts this semester',
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2,
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None,
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'best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester')]}
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<a class="navbar-brand" href="week48-bs.html">Week 48: Gradient boosting and summary of course</a>
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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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<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
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<ul class="dropdown-menu">
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<!-- navigation toc: --> <li><a href="._week48-bs001.html#overview-of-week-48" style="font-size: 80%;">Overview of week 48</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs002.html#lecture-monday-november-25" style="font-size: 80%;">Lecture Monday, November 25</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs003.html#lab-sessions" style="font-size: 80%;">Lab sessions</a></li>
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<!-- 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>
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<!-- 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>
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<!-- 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>
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<!-- 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>
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<!-- 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>
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<!-- 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>
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<!-- 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>
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<!-- navigation toc: --> <li><a href="._week48-bs011.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="._week48-bs012.html#adaptive-boosting-adaboost" style="font-size: 80%;">Adaptive Boosting, AdaBoost</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs013.html#building-up-adaboost" style="font-size: 80%;">Building up AdaBoost</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs014.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="._week48-bs015.html#basic-steps-of-adaboost" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs016.html#adaboost-examples" style="font-size: 80%;">AdaBoost Examples</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs017.html#making-an-adaboost-code-yourself" style="font-size: 80%;">Making an ADAboost code yourself</a></li>
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<!-- 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>
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<!-- 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>
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<!-- navigation toc: --> <li><a href="._week48-bs020.html#steepest-descent-example" style="font-size: 80%;">Steepest Descent Example</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs021.html#gradient-boosting-algorithm" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs022.html#gradient-boosting-examples-of-regression" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs023.html#gradient-boosting-classification-example" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs024.html#xgboost-extreme-gradient-boosting" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs025.html#xgboost-on-the-cancer-data" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
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<!-- 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>
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<!-- navigation toc: --> <li><a href="._week48-bs027.html#summary-of-course" style="font-size: 80%;">Summary of course</a></li>
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<!-- 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>
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<!-- 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>
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<!-- 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>
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<!-- navigation toc: --> <li><a href="._week48-bs031.html#machine-learning" style="font-size: 80%;">Machine learning</a></li>
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<!-- 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>
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<!-- navigation toc: --> <li><a href="._week48-bs033.html#perspective-on-machine-learning" style="font-size: 80%;">Perspective on Machine Learning</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs034.html#machine-learning-research" style="font-size: 80%;">Machine Learning Research</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs035.html#starting-your-machine-learning-project" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs036.html#choose-a-model-and-algorithm" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs037.html#preparing-your-data" style="font-size: 80%;">Preparing Your Data</a></li>
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<!-- 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>
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<!-- navigation toc: --> <li><a href="._week48-bs039.html#optimization-methods-and-hyperparameters" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs040.html#resampling" style="font-size: 80%;">Resampling</a></li>
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<!-- 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>
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<!-- navigation toc: --> <li><a href="._week48-bs042.html#additional-courses-of-interest" style="font-size: 80%;">Additional courses of interest</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs043.html#what-s-the-future-like" style="font-size: 80%;">What's the future like?</a></li>
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<!-- 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>
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<!-- navigation toc: --> <li><a href="._week48-bs045.html#why-boltzmann-machines" style="font-size: 80%;">Why Boltzmann machines?</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs046.html#boltzmann-machines" style="font-size: 80%;">Boltzmann Machines</a></li>
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<!-- 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>
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<!-- navigation toc: --> <li><a href="._week48-bs048.html#boltzmann-machines-bm" style="font-size: 80%;">Boltzmann machines (BM)</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs049.html#a-standard-bm-setup" style="font-size: 80%;">A standard BM setup</a></li>
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<!-- 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>
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<!-- navigation toc: --> <li><a href="._week48-bs051.html#the-network" style="font-size: 80%;">The network</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs052.html#goals" style="font-size: 80%;">Goals</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs053.html#joint-distribution" style="font-size: 80%;">Joint distribution</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs054.html#network-elements-the-energy-function" style="font-size: 80%;">Network Elements, the energy function</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs055.html#defining-different-types-of-rbms" style="font-size: 80%;">Defining different types of RBMs</a></li>
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<h1>Week 48: Gradient boosting and summary of course</h1>
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<!-- author(s): Morten Hjorth-Jensen -->
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<b>Morten Hjorth-Jensen</b>
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<b>Department of Physics and Center for Computing in Science Education, University of Oslo, Norway</b>
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<h4>Nov 25, 2024</h4>
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