Morten Hjorth-Jensen
662 lines
24 KiB
HTML
662 lines
24 KiB
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<h1 class="site-logo" id="site-title">Applied Data Analysis and Machine Learning</h1>
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Applied Data Analysis and Machine Learning
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<p aria-level="2" class="caption" role="heading">
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About the course
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Teaching schedule with links to material
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<a class="current reference internal" href="#">
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Teachers and Grading
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Textbooks
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<p aria-level="2" class="caption" role="heading">
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<span class="caption-text">
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Review of Statistics with Resampling Techniques and Linear Algebra
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<a class="reference internal" href="statistics.html">
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1. Elements of Probability Theory and Statistical Data Analysis
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="linalg.html">
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2. Linear Algebra, Handling of Arrays and more Python Features
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</a>
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</li>
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</ul>
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<p aria-level="2" class="caption" role="heading">
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<span class="caption-text">
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From Regression to Support Vector Machines
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</span>
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3. Linear Regression
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</a>
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</li>
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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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</li>
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<li class="toctree-l1">
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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>
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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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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="chapteroptimization.html">
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7. Optimization, the central part of any Machine Learning algortithm
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="chapter5.html">
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8. Support Vector Machines, overarching aims
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</a>
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</li>
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</ul>
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<p aria-level="2" class="caption" role="heading">
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<span class="caption-text">
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Decision Trees, Ensemble Methods and Boosting
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</span>
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</p>
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9. Decision trees, overarching aims
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</a>
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<a class="reference internal" href="chapter7.html">
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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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</li>
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</ul>
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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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</a>
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12. Clustering and Unsupervised Learning
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<p aria-level="2" class="caption" role="heading">
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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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</a>
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15. Solving Differential Equations with Deep Learning
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</a>
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16. Convolutional Neural Networks
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17. Recurrent neural networks: Overarching view
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</a>
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<p aria-level="2" class="caption" role="heading">
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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: Linear Regression and Statistical interpretations
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</a>
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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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</a>
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<a class="reference internal" href="exercisesweek42.html">
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Exercises week 42
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Week 42 Constructing a Neural Network code with examples
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</a>
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<a class="reference internal" href="additionweek42.html">
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Exercises Week 42: Logistic Regression and Optimization, reminders from week 38 and week 40
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</a>
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Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
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Exercises week 43
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Week 44, Convolutional Neural Networks (CNN)
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Projects
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Project 1 on Machine Learning, deadline October 7 (midnight), 2024
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</a>
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<a class="reference internal" href="project2.html">
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Project 2 on Machine Learning, deadline November 4 (Midnight)
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Instructor information
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Teaching Assistants Fall semester 2023
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Practicalities
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Grading
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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">
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<a class="reference internal nav-link" href="#instructor-information">
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Instructor information
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<li class="toc-h2 nav-item toc-entry">
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<a class="reference internal nav-link" href="#teaching-assistants-fall-semester-2023">
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Teaching Assistants Fall semester 2023
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</a>
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<li class="toc-h2 nav-item toc-entry">
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<a class="reference internal nav-link" href="#practicalities">
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Practicalities
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<li class="toc-h2 nav-item toc-entry">
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<a class="reference internal nav-link" href="#grading">
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Grading
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<li class="toc-h3 nav-item toc-entry">
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<a class="reference internal nav-link" href="#in-summary">
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In summary
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<div class="tex2jax_ignore mathjax_ignore section" id="teachers-and-grading">
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<h1>Teachers and Grading<a class="headerlink" href="#teachers-and-grading" title="Permalink to this headline">¶</a></h1>
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<div class="section" id="instructor-information">
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<h2>Instructor information<a class="headerlink" href="#instructor-information" title="Permalink to this headline">¶</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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<div class="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="Permalink to this headline">¶</a></h2>
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<ul class="simple">
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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>Daniel Haas Becattini Lima, <a class="reference external" href="mailto:d.h.b.lima%40fys.uio.no">d<span>.</span>h<span>.</span>b<span>.</span>lima<span>@</span>fys<span>.</span>uio<span>.</span>no</a></p></li>
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<li><p>Adam Jakobsen, <a class="reference external" href="mailto:adam.jakobsen%40fys.uio.no">adam<span>.</span>jakobsen<span>@</span>fys<span>.</span>uio<span>.</span>no</a></p></li>
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<li><p>Fahimeh Najafi, <a class="reference external" href="mailto:fahimeh.najafi%40fys.uio.no">fahimeh<span>.</span>najafi<span>@</span>fys<span>.</span>uio<span>.</span>no</a></p></li>
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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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<div class="section" id="practicalities">
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<h2>Practicalities<a class="headerlink" href="#practicalities" title="Permalink to this headline">¶</a></h2>
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<ol class="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 Thursdays we have a regular lecture. These lectures start at 1215pm and end at 2pm. The lectures are recorded.</p></li>
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<li><p>Three projects which are graded and count 1/3 each of the final grade;</p></li>
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<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>
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<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>
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<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>
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<div class="section" id="grading">
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<h2>Grading<a class="headerlink" href="#grading" title="Permalink to this headline">¶</a></h2>
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<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>
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<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>
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<ul class="simple">
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<li><p>92-100 points: A</p></li>
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<li><p>77-91 points: B</p></li>
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<li><p>58-76 points: C</p></li>
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<li><p>46-57 points: D</p></li>
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<li><p>40-45 points: E</p></li>
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<li><p>0-39 points: F-failed</p></li>
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</ul>
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<div class="section" id="in-summary">
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<h3>In summary<a class="headerlink" href="#in-summary" title="Permalink to this headline">¶</a></h3>
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<table class="colwidths-auto table">
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<thead>
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<tr class="row-odd"><th class="head"><p>Activity</p></th>
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<th class="head"><p>Percentage of total score</p></th>
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</thead>
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<tbody>
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<tr class="row-even"><td><p>First project, <em>due October 9</em></p></td>
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<td><p>33.3% (1/3)</p></td>
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</tr>
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<tr class="row-odd"><td><p>Second project, <em>due November 6</em></p></td>
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<td><p>33.3% (1.3)</p></td>
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</tr>
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<tr class="row-even"><td><p>Third project, <em>due December 11</em></p></td>
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<td><p>33.3% (1/3)</p></td>
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</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>
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<td><p>10%</p></td>
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</tbody>
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</table>
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<p>The first weekly exercise set is scheduled for week 35.</p>
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