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‎doc/pub/week48/html/week48-bs.html

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<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
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<meta name="generator" content="DocOnce: https://github.com/doconce/doconce/" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<meta name="description" content="Week 48: Support Vector Machines and Summary of course">
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<title>Week 48: Support Vector Machines and Summary of course</title>
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<meta name="description" content="Week 48: Gradient boosting and summary of course">
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<title>Week 48: Gradient boosting and summary of course</title>
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<!-- Bootstrap style: bootstrap -->
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<!-- doconce format html week48.do.txt --html_style=bootstrap --pygments_html_style=default --html_admon=bootstrap_panel --html_output=week48-bs --no_mako -->
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<link href="https://netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css" rel="stylesheet">
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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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('Which activation and weights to choose in neural networks',
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None,
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'which-activation-and-weights-to-choose-in-neural-networks'),
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<span class="icon-bar"></span>
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<span class="icon-bar"></span>
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</button>
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<a class="navbar-brand" href="week48-bs.html">Week 48: Support Vector Machines and Summary of course</a>
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<a class="navbar-brand" href="week48-bs.html">Week 48: Gradient boosting and summary of course</a>
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</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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<!-- navigation toc: --> <li><a href="._week48-bs032.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-bs033.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-bs034.html#preparing-your-data" style="font-size: 80%;">Preparing Your Data</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs035.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-bs035.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-bs036.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-bs037.html#resampling" style="font-size: 80%;">Resampling</a></li>
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<!-- navigation toc: --> <li><a href="._week48-bs038.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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<!-- ------------------- main content ---------------------- -->
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<div class="jumbotron">
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<center>
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<h1>Week 48: Support Vector Machines and Summary of course</h1>
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<h1>Week 48: Gradient boosting and summary of course</h1>
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</center> <!-- document title -->
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<!-- author(s): Morten Hjorth-Jensen -->
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</center>
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<!-- institution -->
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<center>
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<b>Department of Physics, University of Oslo, Norway</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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</center>
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<br>
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<center>
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<h4>Nov 21, 2024</h4>
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<h4>Nov 23, 2024</h4>
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</center> <!-- date -->
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<br>
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‎doc/pub/week48/html/week48-reveal.html

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<meta name="description" content="Week 48: Support Vector Machines and Summary of course">
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<title>Week 48: Support Vector Machines and Summary of course</title>
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<meta name="description" content="Week 48: Gradient boosting and summary of course">
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<title>Week 48: Gradient boosting and summary of course</title>
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<!-- reveal.js: https://lab.hakim.se/reveal-js/ -->
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<section>
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<!-- ------------------- main content ---------------------- -->
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<center>
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<h1 style="text-align: center;">Week 48: Support Vector Machines and Summary of course</h1>
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<h1 style="text-align: center;">Week 48: Gradient boosting and summary of course</h1>
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</center> <!-- document title -->
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<!-- author(s): Morten Hjorth-Jensen -->
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<!-- institution -->
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<b>Department of Physics, University of Oslo, Norway</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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</center>
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<br>
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<h4>Nov 21, 2024</h4>
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<h4>Nov 23, 2024</h4>
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</center> <!-- date -->
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<br>
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<p><li> Work and Discussion of project 3</li>
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<p><li> Last weekly exercise,</li>
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<p><li> Last weekly exercise</li>
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<p><li> Lab sessions at usual times.</li>
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<p><li> For the week of December 2-6, lab sessions atart at 10am and end 4pm, room F&#216;434, Tuesday and Wednesday</li>
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</ul>
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</div>
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<b>Plans for the lecture Monday 25 November, with video suggestions etc</b>
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<p>
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<ol>
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<p><li> Bossting and gradient boosting and ensemble models</li>
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<p><li> Boosting and gradient boosting and ensemble models</li>
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<p><li> Summary of course</li>
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<p><li> Readings and Videos:
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<ol type="a"></li>
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<p><li> Gradient methods for data optimization</li>
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<p><li> Estimation of errors using cross-validation, bootstrapping and jackknife methods;</li>
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<p><li> Practical optimization using Singular-value decomposition and least squares for parameterizing data.</li>
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<p><li> Principal Component Analysis to reduce the number of features.</li>
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<p><li> Not discussed: Principal Component Analysis to reduce the number of features.</li>
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</ol>
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</section>
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<p><li> Boosting and gradient boosting</li>
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</ol>
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<p>
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<p><li> Support vector machines
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<p><li> Not discussed this year: Support vector machines
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<p><li> Binary classification and multiclass classification</li>
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<p><li> Kernel methods</li>
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<ul>
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<p><li> Understand linear methods for regression and classification;</li>
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<p><li> Learn about neural network;</li>
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<p><li> Learn about bagging, boosting and trees</li>
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<p><li> Support vector machines</li>
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<p><li> Learn about bagging, boosting and trees
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<!-- * Support vector machines --></li>
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<p><li> Learn about basic data analysis;</li>
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<p><li> Be capable of extending the acquired knowledge to other systems and cases;</li>
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<p><li> Have an understanding of central algorithms used in data analysis and machine learning;</li>
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<p><li> Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++.</li>
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<p><li> Work on numerical projects to illustrate the theory. The projects play a central role.</li>
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</ul>
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</section>
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<p><li> Rapidly emerging application area</li>
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<p><li> Experiment AND theory are evolving in many many fields. Still many low-hanging fruits.</li>
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<p><li> Experiment AND theory are evolving in many many fields.</li>
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<p><li> Requires education/retraining for more widespread adoption</li>
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<p><li> A lot of &#8220;word-of-mouth&#8221; development methods</li>
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<ol>
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<p><li> Identify problem type: classification, regression</li>
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<p><li> Consider your data carefully</li>
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<p><li> Choose a simple model that fits 1. and 2.</li>
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<p><li> Choose a simple model that fits 1 and 2</li>
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<p><li> Consider your data carefully again! Think of data representation more carefully.</li>
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<p><li> Based on your results, feedback loop to earliest possible point</li>
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</ol>
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</section>
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<section>
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<h2 id="which-activation-and-weights-to-choose-in-neural-networks">Which Activation and Weights to Choose in Neural Networks </h2>
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<h2 id="which-activation-and-weights-to-choose-in-neural-networks">Which activation and weights to choose in neural networks </h2>
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<ol>
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<p><li> RELU? ELU?</li>
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<p><li> RELU? ELU? GELU? etc</li>
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<p><li> Sigmoid or Tanh?</li>
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<p><li> Set all weights to 0?</li>
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<section>
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<h2 id="other-courses-on-data-science-and-machine-learning-at-uio">Other courses on Data science and Machine Learning at UiO </h2>
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<p>The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/" target="_blank"><tt>https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/</tt></a> gives an excellent overview of courses on Machine learning at UiO.</p>
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<p><li> <a href="http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html" target="_blank">STK2100 Machine learning and statistical methods for prediction and classification</a>.</li>
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<p><li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html" target="_blank">IN3050/IN4050 Introduction to Artificial Intelligence and Machine Learning</a>. Introductory course in machine learning and AI with an algorithmic approach.</li>
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<p><li> <a href="https://www.uio.no/studier/emner/matnat/fys/FYS5429/index-eng.html" target="_blank">FYS5429 &#8211; Advanced machine learning and data analysis for the physical sciences</a></li>
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<p><li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html" target="_blank">IN3050/IN4050 Introduction to Artificial Intelligence and Machine Learning</a>. Introductory course in machine learning and AI</li>
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<p><li> <a href="http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html" target="_blank">STK-INF3000/4000 Selected Topics in Data Science</a>. The course provides insight into selected contemporary relevant topics within Data Science.</li>
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<p><li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html" target="_blank">IN4080 Natural Language Processing</a>. Probabilistic and machine learning techniques applied to natural language processing.</li>
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<p><li> <a href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html" target="_blank">STK-IN4300 &#8211; Statistical learning methods in Data Science</a>. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.</li>
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<p><li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html" target="_blank">IN-STK5000 Adaptive Methods for Data-Based Decision Making</a>. Methods for adaptive collection and processing of data based on machine learning techniques.</li>
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<p><li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN5400/" target="_blank">IN5400/INF5860 &#8211; Machine Learning for Image Analysis</a>. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.</li>
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<p><li> <a href="https://www.uio.no/studier/emner/matnat/its/TEK5040/" target="_blank">TEK5040 &#8211; Dyp l&#230;ring for autonome systemer</a>. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.</li>
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<p><li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html" target="_blank">IN-STK5000 Responsible Data Science</a>. Methods for adaptive collection and processing of data based on machine learning techniques.</li>
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<p><li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN4310/index.html" target="_blank">IN4310 &#8211; Machine Learning for Image Analysis</a>. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.</li>
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<p><li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN5310/index.html" target="_blank">IN5310 &#8211; Advanced Deep Learning for Image Analysis</a></li>
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<p><li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN5490/index.html" target="_blank">IN5490 &#8211; Advanced Topics in Artificial Intelligence for Intelligent Systems</a></li>
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<p><li> <a href="https://www.uio.no/studier/emner/matnat/its/TEK5040/" target="_blank">TEK5040 &#8211; Deep learning for autonomous systems</a>. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.</li>
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</section>
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