Morten Hjorth-Jensen
635 lines
30 KiB
HTML
635 lines
30 KiB
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Applied Data Analysis and Machine Learning
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">About the course</span></p>
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<li class="toctree-l1"><a class="reference internal" href="schedule.html">Course setting</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Review of Statistics with Resampling Techniques and Linear Algebra</span></p>
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<li class="toctree-l1"><a class="reference internal" href="statistics.html">1. Elements of Probability Theory and Statistical Data Analysis</a></li>
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<li class="toctree-l1"><a class="reference internal" href="linalg.html">2. Linear Algebra, Handling of Arrays and more Python Features</a></li>
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</ul>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">From Regression to Support Vector Machines</span></p>
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<li class="toctree-l1"><a class="reference internal" href="chapter1.html">3. Linear Regression</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapter2.html">4. Ridge and Lasso Regression</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapter3.html">5. Resampling Methods</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapter4.html">6. Logistic Regression</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapteroptimization.html">7. Optimization, the central part of any Machine Learning algortithm</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapter5.html">8. Support Vector Machines, overarching aims</a></li>
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</ul>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Decision Trees, Ensemble Methods and Boosting</span></p>
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<li class="toctree-l1"><a class="reference internal" href="chapter6.html">9. Decision trees, overarching aims</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapter7.html">10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods</a></li>
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</ul>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Dimensionality Reduction</span></p>
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<li class="toctree-l1"><a class="reference internal" href="chapter8.html">11. Basic ideas of the Principal Component Analysis (PCA)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="clustering.html">12. Clustering and Unsupervised Learning</a></li>
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</ul>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Deep Learning Methods</span></p>
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<li class="toctree-l1"><a class="reference internal" href="chapter9.html">13. Neural networks</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapter10.html">14. Building a Feed Forward Neural Network</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapter11.html">15. Solving Differential Equations with Deep Learning</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapter12.html">16. Convolutional Neural Networks</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapter13.html">17. Recurrent neural networks: Overarching view</a></li>
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</ul>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Weekly material, notes and exercises</span></p>
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<ul class="current nav bd-sidenav">
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek34.html">Exercises week 34</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week34.html">Week 34: Introduction to the course, Logistics and Practicalities</a></li>
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek35.html">Exercises week 35</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week35.html">Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression</a></li>
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek36.html">Exercises week 36</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week36.html">Week 36: Linear Regression and Gradient descent</a></li>
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek37.html">Exercises week 37</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week37.html">Week 37: Gradient descent methods</a></li>
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek38.html">Exercises week 38</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
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<li class="toctree-l1 current active"><a class="current reference internal" href="#">Exercises week 39</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week39.html">Week 39: Resampling methods and logistic regression</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek41.html">Exercises week 41</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week42.html">Week 42 Constructing a Neural Network code with examples</a></li>
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek42.html">Exercises week 42</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week43.html">Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations</a></li>
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek43.html">Exercises week 43</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
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<li class="toctree-l1"><a class="reference internal" href="project1.html">Project 1 on Machine Learning, deadline October 6 (midnight), 2025</a></li>
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<li class="toctree-l1"><a class="reference internal" href="project2.html">Project 2 on Machine Learning, deadline November 10 (Midnight)</a></li>
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<h1>Exercises week 39</h1>
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<h2> Contents </h2>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#getting-started-with-project-1">Getting started with project 1</a><ul class="nav section-nav flex-column">
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#learning-goals">Learning goals</a></li>
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#deliverables">Deliverables</a></li>
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</ul>
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</li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-1-creating-the-report-document">Exercise 1: Creating the report document</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-2-adding-good-figures">Exercise 2: Adding good figures</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-3-writing-an-abstract-and-introduction">Exercise 3: Writing an abstract and introduction</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-4-making-the-code-available-and-presentable">Exercise 4: Making the code available and presentable</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-5-referencing">Exercise 5: Referencing</a></li>
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<article class="bd-article">
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<section class="tex2jax_ignore mathjax_ignore" id="exercises-week-39">
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<h1>Exercises week 39<a class="headerlink" href="#exercises-week-39" title="Link to this heading">#</a></h1>
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<section id="getting-started-with-project-1">
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<h2>Getting started with project 1<a class="headerlink" href="#getting-started-with-project-1" title="Link to this heading">#</a></h2>
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<p>The aim of the exercises this week is to aid you in getting started with writing the report. This will be discussed during the lab sessions as well.</p>
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<p>A short feedback to the this exercise will be available before the project deadline. And you can reuse these elements in your final report.</p>
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<section id="learning-goals">
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<h3>Learning goals<a class="headerlink" href="#learning-goals" title="Link to this heading">#</a></h3>
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<p>After completing these exercises, you will know how to</p>
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<ul class="simple">
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<li><p>Create a properly formatted report in Overleaf</p></li>
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<li><p>Select and present graphs for a scientific report</p></li>
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<li><p>Write an abstract and introduction for a scientific report</p></li>
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</ul>
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</section>
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<section id="deliverables">
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<h3>Deliverables<a class="headerlink" href="#deliverables" title="Link to this heading">#</a></h3>
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<p>Complete the following exercises while working in an Overleaf project. Then, in canvas, include</p>
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<ul class="simple">
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<li><p>An exported PDF of the report draft you have been working on.</p></li>
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<li><p>A comment linking to the github repository used in exercise 4.</p></li>
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</ul>
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</section>
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</section>
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<section id="exercise-1-creating-the-report-document">
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<h2>Exercise 1: Creating the report document<a class="headerlink" href="#exercise-1-creating-the-report-document" title="Link to this heading">#</a></h2>
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<p>We require all projects to be formatted as proper scientific reports, and this includes using LaTeX for typesetting. We strongly recommend that you use the online LaTeX editor Overleaf, as it is much easier to start using, and has excellent support for collaboration.</p>
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<p><strong>a)</strong> Create an account on <a class="reference external" href="http://Overleaf.com">Overleaf.com</a>, or log in using SSO with your UiO email.</p>
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<p><strong>b)</strong> Download <a class="reference external" href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/LectureNotes/data/FYS_STK_Template.zip">this</a> template project.</p>
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<p><strong>c)</strong> Create a new Overleaf project with the correct formatting by uploading the template project.</p>
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<p><strong>d)</strong> Read the general guideline for writing a report, which can be found at <a class="github reference external" href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md">CompPhysics/MachineLearning</a>.</p>
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<p><strong>e)</strong> Look at the provided example of an earlier project, found at <a class="github reference external" href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/ReportExample/ReportExampleDanielBH.pdf">CompPhysics/MachineLearning</a></p>
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</section>
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<section id="exercise-2-adding-good-figures">
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<h2>Exercise 2: Adding good figures<a class="headerlink" href="#exercise-2-adding-good-figures" title="Link to this heading">#</a></h2>
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<p><strong>a)</strong> Using what you have learned so far in this course, create a plot illustrating the Bias-Variance trade-off. Make sure the lines and axes are labeled, with font size being the same as in the text.</p>
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<p><strong>b)</strong> Add this figure to the results section of your document, with a caption that describes it. A reader should be able to understand the figure with only its contents and caption.</p>
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<p><strong>c)</strong> Refer to the figure in your text using \ref.</p>
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<p><strong>d)</strong> Create a heatmap showing the MSE of a Ridge regression model for various polynomial degrees and lambda values. Make sure the axes are labeled, and that the title or colorbar describes what is plotted.</p>
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<p><strong>e)</strong> Add this second figure to your document with a caption and reference in the text. All figures in the final report must be captioned and be referenced and used in the text.</p>
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</section>
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<section id="exercise-3-writing-an-abstract-and-introduction">
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<h2>Exercise 3: Writing an abstract and introduction<a class="headerlink" href="#exercise-3-writing-an-abstract-and-introduction" title="Link to this heading">#</a></h2>
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<p>Although much of your project 1 results are not done yet, we want you to write an abstract and introduction to get you started on writing the report. It is generally a good idea to write a lot of a report before finishing all of the results, as you get a better understanding of your methods and inquiry from doing so, along with saving a lot of time. Where you would typically describe results in the abstract, instead make something up, just this once.</p>
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<p><strong>a)</strong> Read the guidelines on abstract and introduction before you start.</p>
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<p><strong>b)</strong> Write an abstract for project 1 in your report.</p>
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<p><strong>c)</strong> Write an introduction for project 1 in your report.</p>
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</section>
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<section id="exercise-4-making-the-code-available-and-presentable">
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<h2>Exercise 4: Making the code available and presentable<a class="headerlink" href="#exercise-4-making-the-code-available-and-presentable" title="Link to this heading">#</a></h2>
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<p>A central part of the report is the code you write to implement the methods and generate the results. To get points for the code-part of the project, you need to make your code avaliable and presentable.</p>
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<p><strong>a)</strong> Create a github repository for project 1, or create a dedicated folder for project 1 in a github repository. Only one person in your group needs to do this.</p>
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<p><strong>b)</strong> Add a PDF of the report to this repository, after completing exercises 1-3</p>
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<p><strong>c)</strong> Add a folder named Code, where you can put python files for your functions and notebooks for reproducing your results.</p>
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<p><strong>d)</strong> Add python files for functions, and a notebook to produce the figures in exercise 2, to the Code folder. Remember to use a seed for generating random data and for train-test splits.</p>
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<p><strong>e)</strong> Create a README file in the repository or project folder with</p>
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<ul class="simple">
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<li><p>the name of the group members</p></li>
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<li><p>a short description of the project</p></li>
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<li><p>a description of how to install the required packages to run your code from a requirements.txt file</p></li>
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<li><p>names and descriptions of the various notebooks in the Code folder and the results they produce</p></li>
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</ul>
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</section>
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<section id="exercise-5-referencing">
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<h2>Exercise 5: Referencing<a class="headerlink" href="#exercise-5-referencing" title="Link to this heading">#</a></h2>
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<p><strong>a)</strong> Add a reference to Hastie et al. using your preferred referencing style. See <a class="reference external" href="https://www.sokogskriv.no/referansestiler/">https://www.sokogskriv.no/referansestiler/</a> for an overview of styles.</p>
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<p><strong>b)</strong> Add a reference to sklearn like this: <a class="reference external" href="https://scikit-learn.org/stable/about.html#citing-scikit-learn">https://scikit-learn.org/stable/about.html#citing-scikit-learn</a></p>
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<p><strong>c)</strong> Make a prompt to your LLM of choice, and upload the exported conversation to your GitHub repository for the project.</p>
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<p><strong>d)</strong> At the end of the methods section of the report, write a one paragraph declaration on how and for what you have used the LLM. Link to the log on GitHub.</p>
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</section>
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</section>
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