Morten Hjorth-Jensen
544 lines
18 KiB
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544 lines
18 KiB
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Applied Data Analysis and Machine Learning
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About the course
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Teachers and Grading
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Review of Statistics with Resampling Techniques and Linear Algebra
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1. Elements of Probability Theory and Statistical Data Analysis
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</a>
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2. Linear Algebra, Handling of Arrays and more Python Features
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From Regression to Support Vector Machines
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3. Linear Regression
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</a>
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<li class="toctree-l1">
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<a class="reference internal" href="chapter2.html">
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4. Ridge and Lasso Regression
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</a>
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<a class="reference internal" href="chapter3.html">
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5. Resampling Methods
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</a>
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<li class="toctree-l1">
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<a class="reference internal" href="chapter4.html">
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6. Logistic Regression
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7. Optimization, the central part of any Machine Learning algortithm
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8. Support Vector Machines, overarching aims
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<p aria-level="2" class="caption" role="heading">
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Decision Trees, Ensemble Methods and Boosting
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9. Decision trees, overarching aims
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10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
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</a>
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<p aria-level="2" class="caption" role="heading">
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Dimensionality Reduction
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11. Basic ideas of the Principal Component Analysis (PCA)
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12. Clustering and Unsupervised Learning
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Deep Learning Methods
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13. Neural networks
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14. Building a Feed Forward Neural Network
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15. Solving Differential Equations with Deep Learning
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16. Convolutional Neural Networks
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17. Recurrent neural networks: Overarching view
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Weekly material, notes and exercises
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Exercises week 34
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Week 34: Introduction to the course, Logistics and Practicalities
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Exercises week 35
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Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression
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Exercises week 36
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Week 36: Statistical interpretation of Linear Regression and Resampling techniques
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Exercises week 37
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Week 37: Statistical interpretations and Resampling Methods
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Exercises week 38
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Week 38: Logistic Regression and Optimization
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Exercises week 39
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Week 39: Optimization and Gradient Methods
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Week 40: Gradient descent methods (continued) and start Neural networks
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Exercises week 41
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Week 41 Neural networks and constructing a neural network code
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Exercises week 42
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Week 42 Constructing a Neural Network code with introduction to Tensor flow
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Projects
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Project 1 on Machine Learning, deadline October 9 (midnight), 2023
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Project 2 on Machine Learning, deadline November 13 (Midnight)
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<h1>Exercises week 39</h1>
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<!-- dom:TITLE: Exercises week 39 --><div class="tex2jax_ignore mathjax_ignore section" id="exercises-week-39">
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<h1>Exercises week 39<a class="headerlink" href="#exercises-week-39" title="Permalink to this headline">¶</a></h1>
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<p><strong>September 25-29, 2023</strong></p>
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<p>Date: <strong>Deadline is Sunday October 1 at midnight</strong></p>
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<div class="section" id="overarching-aims-of-the-exercises-this-week">
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<h2>Overarching aims of the exercises this week<a class="headerlink" href="#overarching-aims-of-the-exercises-this-week" title="Permalink to this headline">¶</a></h2>
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<p>The aim of the exercises this week is to aid you in getting started
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with writing the report. This will be discussed during the lab
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sessions as well. One of the lab sessions will be recorded.</p>
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<p>A general guideline can be found at <a class="reference external" href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md">https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md</a>.</p>
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<p>Similarly, an example of an earlier project can be found at <a class="reference external" href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/ReportExample/ReportSample.pdf">https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/ReportExample/ReportSample.pdf</a></p>
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<p>Your task this week is to</p>
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<ol class="simple">
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<li><p>Write an abstract for your project</p></li>
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<li><p>Write an introduction</p></li>
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<li><p>Include references</p></li>
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</ol>
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<p>Ashort feedback to the this exercise will be available after the deadline. And you can reuse these elements in your final report.</p>
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</div>
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</div>
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<script type="text/x-thebe-config">
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{
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requestKernel: true,
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binderOptions: {
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repo: "binder-examples/jupyter-stacks-datascience",
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ref: "master",
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},
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codeMirrorConfig: {
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theme: "abcdef",
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mode: "python"
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},
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kernelOptions: {
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kernelName: "python3",
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path: "./."
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},
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predefinedOutput: true
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}
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</script>
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<script>kernelName = 'python3'</script>
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