Morten Hjorth-Jensen
649 lines
30 KiB
HTML
649 lines
30 KiB
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Applied Data Analysis and Machine Learning
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<li class="toctree-l1"><a class="reference internal" href="schedule.html">Course setting</a></li>
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<li class="toctree-l1 current active"><a class="current reference internal" href="#">Teachers and Grading</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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<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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<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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<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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<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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<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="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="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"><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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<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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<h1>Teachers and Grading</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="#instructor-information">Instructor information</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#teaching-assistants-fall-semester-2023">Teaching Assistants Fall semester 2023</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#practicalities">Practicalities</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#grading">Grading</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="#in-summary">In summary</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="#id1">Practicalities</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#id2">Grading</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#tentative-deadllines-for-projects">Tentative deadllines for projects</a></li>
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<div id="searchbox"></div>
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<article class="bd-article">
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<section class="tex2jax_ignore mathjax_ignore" id="teachers-and-grading">
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<h1>Teachers and Grading<a class="headerlink" href="#teachers-and-grading" title="Link to this heading">#</a></h1>
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<section id="instructor-information">
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<h2>Instructor information<a class="headerlink" href="#instructor-information" title="Link to this heading">#</a></h2>
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<ul class="simple">
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<li><p><em>Name</em>: Morten Hjorth-Jensen</p></li>
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<li><p><em>Email</em>: <a class="reference external" href="mailto:morten.hjorth-jensen%40fys.uio.no">morten<span>.</span>hjorth-jensen<span>@</span>fys<span>.</span>uio<span>.</span>no</a></p></li>
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<li><p><em>Phone</em>: +47-48257387</p></li>
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<li><p><em>Office</em>: Department of Physics, University of Oslo, Eastern wing, room FØ470</p></li>
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<li><p><em>Office hours</em>: <em>Anytime</em>! Feel free to send an email for planning. Both in person meetings or digital meetings are possible.</p></li>
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</ul>
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</section>
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<section id="teaching-assistants-fall-semester-2023">
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<h2>Teaching Assistants Fall semester 2023<a class="headerlink" href="#teaching-assistants-fall-semester-2023" title="Link to this heading">#</a></h2>
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<ul class="simple">
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<li><p>Ida Torkjellsdatter Storehaug, <a class="reference external" href="mailto:i.t.storehaug%40fys.uio.no">i<span>.</span>t<span>.</span>storehaug<span>@</span>fys<span>.</span>uio<span>.</span>no</a></p></li>
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<li><p>Mia-Katrin Ose Kvalsund, <a class="reference external" href="mailto:m.k.o.kvalsund%40fys.uio.no">m<span>.</span>k<span>.</span>o<span>.</span>kvalsund<span>@</span>fys<span>.</span>uio<span>.</span>no</a></p></li>
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<li><p>Eivind Støland, <a class="reference external" href="mailto:eivinsto%40fys.uio.no">eivinsto<span>@</span>fys<span>.</span>uio<span>.</span>no</a></p></li>
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<li><p>Karl Henrik Fredly, <a class="reference external" href="mailto:k.h.fredly%40fys.uio.no">k<span>.</span>h<span>.</span>fredly<span>@</span>fys<span>.</span>uio<span>.</span>no</a></p></li>
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<li><p>Eir Eline Hørlyk, <a class="reference external" href="mailto:e.e.horlyk%40fys.uio.no">e<span>.</span>e<span>.</span>horlyk<span>@</span>fys<span>.</span>uio<span>.</span>no</a></p></li>
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<li><p>Britt S. Haanen, <a class="reference external" href="mailto:b.s.m.haanen%40fys.uio.no">b<span>.</span>s<span>.</span>m<span>.</span>haanen<span>@</span>fys<span>.</span>uio<span>.</span>no</a></p></li>
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</ul>
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</section>
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<section id="practicalities">
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<h2>Practicalities<a class="headerlink" href="#practicalities" title="Link to this heading">#</a></h2>
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<ol class="arabic simple">
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<li><p>The sessions on Tuesdays and Wednesdays last four hours for each group (four in total) and will include lectures in a flipped mode (promoting active learning) and work on exercices and projects. The sessions will begin with lectures and questions and answers about the material to be covered every week.</p></li>
|
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<li><p>There are four groups, Tuesdays 815am-12pm and 1215pm-4pm and Wednesdays 815am-12pm and 1215pm-4pm. Please sign up as soon as possible for one of the groups. Max capacity per group is 30-40 participants.</p></li>
|
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<li><p>On Mondays we have a regular lecture. The lecture starts at 215pm and ends at 4pm. All lectures are recorded.</p></li>
|
|
<li><p>Three projects which are graded and count 1/3 each of the final grade;</p></li>
|
|
<li><p>A selected number of weekly assignments. The weekly assignments can be handed in and for all assignments you can get an extra score of 10 points to the final grade.</p></li>
|
|
<li><p>The course is part of the CS Master of Science program, but is open to other bachelor and Master of Science students at the University of Oslo;</p></li>
|
|
<li><p>The course is offered as a so-called <em>cloned</em> course, FYS-STK4155 at the Master of Science level and FYS-STK3155 as a senior undergraduate)course;</p></li>
|
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<li><p>Videos of teaching material are available via the links at <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/web/course.html">https://compphysics.github.io/MachineLearning/doc/web/course.html</a>;</p></li>
|
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<li><p>Weekly email with summary of activities will be mailed to all participants;</p></li>
|
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</ol>
|
|
</section>
|
|
<section id="grading">
|
|
<h2>Grading<a class="headerlink" href="#grading" title="Link to this heading">#</a></h2>
|
|
<p>Grading scale: Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. There are three projects which are graded and each project counts 1/3 of the final grade. The total score is thus the average from all three projects.</p>
|
|
<p>The final number of points is based on the average of all projects (including eventual additional points) and the grade follows the following table:</p>
|
|
<ul class="simple">
|
|
<li><p>92-100 points: A</p></li>
|
|
<li><p>77-91 points: B</p></li>
|
|
<li><p>58-76 points: C</p></li>
|
|
<li><p>46-57 points: D</p></li>
|
|
<li><p>40-45 points: E</p></li>
|
|
<li><p>0-39 points: F-failed</p></li>
|
|
</ul>
|
|
<section id="in-summary">
|
|
<h3>In summary<a class="headerlink" href="#in-summary" title="Link to this heading">#</a></h3>
|
|
<div class="pst-scrollable-table-container"><table class="table">
|
|
<thead>
|
|
<tr class="row-odd"><th class="head"><p>Activity</p></th>
|
|
<th class="head"><p>Percentage of total score</p></th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr class="row-even"><td><p>First project, <em>due October 6</em></p></td>
|
|
<td><p>33.3% (1/3)</p></td>
|
|
</tr>
|
|
<tr class="row-odd"><td><p>Second project, <em>due November 3</em></p></td>
|
|
<td><p>33.3% (1.3)</p></td>
|
|
</tr>
|
|
<tr class="row-even"><td><p>Third project, <em>due December 8</em></p></td>
|
|
<td><p>33.3% (1/3)</p></td>
|
|
</tr>
|
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<tr class="row-odd"><td><p>Extra Credit (not mandatory), weekly exercise assignments, 10 in total (due each Friday)</p></td>
|
|
<td><p>10%</p></td>
|
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</tr>
|
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</tbody>
|
|
</table>
|
|
</div>
|
|
</section>
|
|
</section>
|
|
<section id="id1">
|
|
<h2>Practicalities<a class="headerlink" href="#id1" title="Link to this heading">#</a></h2>
|
|
<ol class="arabic simple">
|
|
<li><p>The sessions on Tuesdays and Wednesdays last four hours for each group (four in total) and will include lectures in a flipped mode (promoting active learning) and work on exercices and projects. The sessions will begin with lectures and questions and answers about the material to be covered every week.</p></li>
|
|
<li><p>There are four groups, Tuesdays 815am-12pm and 1215pm-4pm and Wednesdays 815am-12pm and 1215pm-4pm. Please sign up as soon as possible for one of the groups. Max capacity per group is 30-40 participants.</p></li>
|
|
<li><p>On Mondays we have a regular lecture. These lectures start at 215pm and ends at 4pm. These lectures are recorded.</p></li>
|
|
<li><p>Three projects which are graded and count 1/3 each of the final grade;</p></li>
|
|
<li><p>A selected number of weekly assignments. The weekly assignments can be handed in and for all assignments you can get an extra score of 20 points to the final grade.</p></li>
|
|
<li><p>The course is offered as a so-called <em>cloned</em> course, FYS-STK4155 at the Master of Science level and FYS-STK3155 as a senior undergraduate)course;</p></li>
|
|
<li><p>Weekly email with summary of activities will be mailed to all participants;</p></li>
|
|
</ol>
|
|
</section>
|
|
<section id="id2">
|
|
<h2>Grading<a class="headerlink" href="#id2" title="Link to this heading">#</a></h2>
|
|
<p>Grading scale: Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. There are three projects which are graded and each project counts 1/3 of the final grade. The total score is thus the average from all three projects.</p>
|
|
<p>The final number of points is based on the average of all projects (including eventual additional points) and the grade follows the following table:</p>
|
|
<ul class="simple">
|
|
<li><p>92-100 points: A</p></li>
|
|
<li><p>77-91 points: B</p></li>
|
|
<li><p>58-76 points: C</p></li>
|
|
<li><p>46-57 points: D</p></li>
|
|
<li><p>40-45 points: E</p></li>
|
|
<li><p>0-39 points: F-failed</p></li>
|
|
</ul>
|
|
</section>
|
|
<section id="tentative-deadllines-for-projects">
|
|
<h2>Tentative deadllines for projects<a class="headerlink" href="#tentative-deadllines-for-projects" title="Link to this heading">#</a></h2>
|
|
<ul class="simple">
|
|
<li><p>First project, due October 6, counts 1/3 of final grade</p></li>
|
|
<li><p>Second project, due November 3, counts 1/3 of final grade</p></li>
|
|
<li><p>Third project, due December 8, counts 1/3 of final grade</p></li>
|
|
<li><p>Extra Credit (not mandatory), weekly exercise assignments, 10 in total (due Fridays), adds one point per exercises set.</p></li>
|
|
</ul>
|
|
</section>
|
|
</section>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#teaching-assistants-fall-semester-2023">Teaching Assistants Fall semester 2023</a></li>
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