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Morten Hjorth-Jensen
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Applied Data Analysis and Machine Learning
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Applied Data Analysis and Machine Learning
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About the course
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</p>
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<ul class="current nav bd-sidenav">
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<li class="toctree-l1 current active"><a class="current reference internal" href="#">Teaching schedule with links to material</a></li>
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<li class="toctree-l1"><a class="reference internal" href="teachers.html">Teachers and Grading</a></li>
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<li class="toctree-l1"><a class="reference internal" href="textbooks.html">Textbooks</a></li>
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<a class="current reference internal" href="#">
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Teaching schedule with links to material
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<li class="toctree-l1">
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<a class="reference internal" href="teachers.html">
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Teachers and Grading
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Textbooks
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</a>
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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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<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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</span>
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</p>
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<ul class="nav bd-sidenav">
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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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<li class="toctree-l1">
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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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<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"><span class="caption-text">From Regression to Support Vector Machines</span></p>
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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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</p>
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<ul class="nav bd-sidenav">
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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">
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<a class="reference internal" href="chapter1.html">
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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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<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"><span class="caption-text">Decision Trees, Ensemble Methods and Boosting</span></p>
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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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|
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9. Decision trees, overarching aims
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<li class="toctree-l1">
|
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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"><span class="caption-text">Dimensionality Reduction</span></p>
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<p aria-level="2" class="caption" role="heading">
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<span class="caption-text">
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Dimensionality Reduction
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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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<li class="toctree-l1">
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<a class="reference internal" href="chapter8.html">
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11. Basic ideas of the Principal Component Analysis (PCA)
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<li class="toctree-l1">
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<a class="reference internal" href="clustering.html">
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12. Clustering and Unsupervised Learning
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</a>
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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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<p aria-level="2" class="caption" role="heading">
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<span class="caption-text">
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Deep Learning Methods
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<ul class="nav bd-sidenav">
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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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<a class="reference internal" href="chapter9.html">
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13. Neural networks
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|
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<a class="reference internal" href="chapter10.html">
|
||||
14. Building a Feed Forward Neural Network
|
||||
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|
||||
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|
||||
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|
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<a class="reference internal" href="chapter11.html">
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||||
15. Solving Differential Equations with Deep Learning
|
||||
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|
||||
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|
||||
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|
||||
<a class="reference internal" href="chapter12.html">
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||||
16. Convolutional Neural Networks
|
||||
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|
||||
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|
||||
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|
||||
<a class="reference internal" href="chapter13.html">
|
||||
17. Recurrent neural networks: Overarching view
|
||||
</a>
|
||||
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|
||||
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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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Weekly material, notes and exercises
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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="exercisesweek35.html">Exercises week 35</a></li>
|
||||
<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>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week36.html">Week 36: Statistical interpretation of Linear Regression and Resampling techniques</a></li>
|
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek37.html">Exercises week 37</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="week37.html">Week 37: Statistical interpretations and Resampling 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: Logistic Regression and Optimization</a></li>
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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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|
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<a class="reference internal" href="week35.html">
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Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression
|
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|
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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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Project 1 on Machine Learning, deadline October 9 (midnight), 2023
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<section class="tex2jax_ignore mathjax_ignore" id="teaching-schedule-with-links-to-material">
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||||
<h1>Teaching schedule with links to material<a class="headerlink" href="#teaching-schedule-with-links-to-material" title="Permalink to this heading">#</a></h1>
|
||||
<div class="tex2jax_ignore mathjax_ignore section" id="teaching-schedule-with-links-to-material">
|
||||
<h1>Teaching schedule with links to material<a class="headerlink" href="#teaching-schedule-with-links-to-material" title="Permalink to this headline">¶</a></h1>
|
||||
<p>This course will be delivered in a hybrid mode, with online lectures and on site or online laboratory sessions.</p>
|
||||
<ol class="arabic simple">
|
||||
<ol class="simple">
|
||||
<li><p>Four lectures per week, Fall semester, 10 ECTS. The lectures are in person but will be recorded and linked to this site and the official University of Oslo website for the course;</p></li>
|
||||
<li><p>Two hours of laboratory sessions for work on computational projects and exercises for each group. There will also be fully digital laboratory sessions for those who cannot attend;</p></li>
|
||||
<li><p>Three projects which are graded and count 1/3 each of the final grade. The deadlines for the projects are October 7 for project 1, November 11 for project 2 and December 9 for project 3.</p></li>
|
||||
@@ -393,8 +446,8 @@ document.write(`
|
||||
<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>
|
||||
<li><p>Weekly emails with summary of activities will be mailed to all participants;</p></li>
|
||||
</ol>
|
||||
<section id="weekly-schedule">
|
||||
<h2>Weekly Schedule<a class="headerlink" href="#weekly-schedule" title="Permalink to this heading">#</a></h2>
|
||||
<div class="section" id="weekly-schedule">
|
||||
<h2>Weekly Schedule<a class="headerlink" href="#weekly-schedule" title="Permalink to this headline">¶</a></h2>
|
||||
<p>For the reading assignments we use the following abbreviations:</p>
|
||||
<ul class="simple">
|
||||
<li><p>GBC: Goodfellow, Bengio, and Courville, Deep Learning</p></li>
|
||||
@@ -402,8 +455,8 @@ document.write(`
|
||||
<li><p>HTF: Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning</p></li>
|
||||
<li><p>AG: Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow</p></li>
|
||||
</ul>
|
||||
</section>
|
||||
</section>
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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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@@ -417,7 +470,7 @@ document.write(`
|
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mode: "python"
|
||||
},
|
||||
kernelOptions: {
|
||||
name: "python3",
|
||||
kernelName: "python3",
|
||||
path: "./."
|
||||
},
|
||||
predefinedOutput: true
|
||||
@@ -425,101 +478,44 @@ document.write(`
|
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</script>
|
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<script>kernelName = 'python3'</script>
|
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<a class='right-next' id="next-link" href="teachers.html" title="next page">
|
||||
<div class="prev-next-info">
|
||||
<p class="prev-next-subtitle">next</p>
|
||||
<p class="prev-next-title">Teachers and Grading</p>
|
||||
</div>
|
||||
<i class="fa-solid fa-angle-right"></i>
|
||||
</div>
|
||||
<i class="fas fa-angle-right"></i>
|
||||
</a>
|
||||
</div>
|
||||
</footer>
|
||||
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="bd-sidebar-secondary bd-toc"><div class="sidebar-secondary-items sidebar-secondary__inner">
|
||||
|
||||
<div class="sidebar-secondary-item">
|
||||
<div class="page-toc tocsection onthispage">
|
||||
<i class="fa-solid fa-list"></i> Contents
|
||||
</div>
|
||||
<nav class="bd-toc-nav page-toc">
|
||||
<ul class="visible nav section-nav flex-column">
|
||||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#weekly-schedule">Weekly Schedule</a></li>
|
||||
</ul>
|
||||
</nav></div>
|
||||
|
||||
</div></div>
|
||||
|
||||
|
||||
</div>
|
||||
<footer class="bd-footer-content">
|
||||
|
||||
<div class="bd-footer-content__inner container">
|
||||
|
||||
<div class="footer-item">
|
||||
|
||||
<p class="component-author">
|
||||
By Morten Hjorth-Jensen
|
||||
</p>
|
||||
|
||||
</div>
|
||||
|
||||
<div class="footer-item">
|
||||
|
||||
<p class="copyright">
|
||||
|
||||
© Copyright 2022.
|
||||
<br/>
|
||||
|
||||
</p>
|
||||
|
||||
</div>
|
||||
|
||||
<div class="footer-item">
|
||||
|
||||
</div>
|
||||
|
||||
<div class="footer-item">
|
||||
|
||||
</div>
|
||||
|
||||
</div>
|
||||
</footer>
|
||||
|
||||
|
||||
</main>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Scripts loaded after <body> so the DOM is not blocked -->
|
||||
<script src="_static/scripts/bootstrap.js?digest=ac02cc09edc035673794"></script>
|
||||
<script src="_static/scripts/pydata-sphinx-theme.js?digest=ac02cc09edc035673794"></script>
|
||||
<footer class="footer">
|
||||
<p>
|
||||
|
||||
By Morten Hjorth-Jensen<br/>
|
||||
|
||||
© Copyright 2021.<br/>
|
||||
</p>
|
||||
</footer>
|
||||
</main>
|
||||
|
||||
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<script src="_static/js/index.be7d3bbb2ef33a8344ce.js"></script>
|
||||
|
||||
<footer class="bd-footer">
|
||||
</footer>
|
||||
</body>
|
||||
</html>
|
||||
Reference in New Issue
Block a user