Morten Hjorth-Jensen
882 lines
35 KiB
HTML
882 lines
35 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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About the course
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<a class="current reference internal" href="#">
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Teaching schedule with links to material
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</a>
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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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</span>
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</p>
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<ul class="nav bd-sidenav">
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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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</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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</p>
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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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</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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<li class="toctree-l1">
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<a class="reference internal" href="chapter6.html">
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9. Decision trees, overarching aims
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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="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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<span class="caption-text">
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Dimensionality Reduction
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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">
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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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</a>
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</li>
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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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</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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Deep Learning Methods
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<a class="reference internal" href="chapter9.html">
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13. Neural networks
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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="chapter10.html">
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14. Building a Feed Forward Neural Network
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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="chapter11.html">
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15. Solving Differential Equations with Deep Learning
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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="chapter12.html">
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16. Convolutional Neural Networks
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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="chapter13.html">
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17. Recurrent neural networks: Overarching view
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<i class="fas fa-list"></i> Contents
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Weekly Schedule
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<li class="toc-h3 nav-item toc-entry">
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<a class="reference internal nav-link" href="#week-34-august-22-26">
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Week 34 August 22-26
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<li class="toc-h3 nav-item toc-entry">
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<a class="reference internal nav-link" href="#week-35-august-29-september-2">
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Week 35 August 29-September 2
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<li class="toc-h3 nav-item toc-entry">
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<a class="reference internal nav-link" href="#week-36-september-5-9">
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Week 36 September 5-9
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<li class="toc-h3 nav-item toc-entry">
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<a class="reference internal nav-link" href="#week-37-september-12-16">
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Week 37 September 12-16
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<li class="toc-h3 nav-item toc-entry">
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<a class="reference internal nav-link" href="#week-38-september-19-23">
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Week 38 September 19-23
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</a>
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<li class="toc-h3 nav-item toc-entry">
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<a class="reference internal nav-link" href="#week-39-september-26-30">
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Week 39 September 26-30
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<li class="toc-h3 nav-item toc-entry">
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<a class="reference internal nav-link" href="#week-40-october-3-7">
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Week 40 October 3-7
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</a>
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<li class="toc-h3 nav-item toc-entry">
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<a class="reference internal nav-link" href="#week-41-october-10-14">
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Week 41 October 10-14
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</a>
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<li class="toc-h3 nav-item toc-entry">
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<a class="reference internal nav-link" href="#week-42-october-17-21">
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Week 42 October 17-21
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</a>
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</li>
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<li class="toc-h3 nav-item toc-entry">
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<a class="reference internal nav-link" href="#week-43-october-24-28">
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Week 43 October 24-28
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</a>
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</li>
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<li class="toc-h3 nav-item toc-entry">
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<a class="reference internal nav-link" href="#week-44-october-31-november-4">
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Week 44 October 31-November 4
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</a>
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</li>
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<li class="toc-h3 nav-item toc-entry">
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<a class="reference internal nav-link" href="#week-45-november-7-11">
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Week 45 November 7-11
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</a>
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</li>
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<li class="toc-h3 nav-item toc-entry">
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<a class="reference internal nav-link" href="#week-46-november-14-18">
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Week 46 November 14-18
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</a>
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</li>
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<li class="toc-h3 nav-item toc-entry">
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<a class="reference internal nav-link" href="#week-47-november-21-25">
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Week 47 November 21-25
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</a>
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</ul>
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<!-- Table of contents that is only displayed when printing the page -->
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<div id="jb-print-docs-body" class="onlyprint">
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<h1>Teaching schedule with links to material</h1>
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<!-- Table of contents -->
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<div id="print-main-content">
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<div id="jb-print-toc">
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<h2> Contents </h2>
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Weekly Schedule
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<ul class="nav section-nav flex-column">
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<li class="toc-h3 nav-item toc-entry">
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<a class="reference internal nav-link" href="#week-34-august-22-26">
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Week 34 August 22-26
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</a>
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</li>
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<li class="toc-h3 nav-item toc-entry">
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<a class="reference internal nav-link" href="#week-35-august-29-september-2">
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Week 35 August 29-September 2
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</a>
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</li>
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<li class="toc-h3 nav-item toc-entry">
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<a class="reference internal nav-link" href="#week-36-september-5-9">
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Week 36 September 5-9
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</a>
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</li>
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<li class="toc-h3 nav-item toc-entry">
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<a class="reference internal nav-link" href="#week-37-september-12-16">
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Week 37 September 12-16
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</a>
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</li>
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<li class="toc-h3 nav-item toc-entry">
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<a class="reference internal nav-link" href="#week-38-september-19-23">
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Week 38 September 19-23
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</a>
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</li>
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<li class="toc-h3 nav-item toc-entry">
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<a class="reference internal nav-link" href="#week-39-september-26-30">
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Week 39 September 26-30
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</a>
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</li>
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<li class="toc-h3 nav-item toc-entry">
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<a class="reference internal nav-link" href="#week-40-october-3-7">
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Week 40 October 3-7
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</a>
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</li>
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<li class="toc-h3 nav-item toc-entry">
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<a class="reference internal nav-link" href="#week-41-october-10-14">
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Week 41 October 10-14
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</a>
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</li>
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<li class="toc-h3 nav-item toc-entry">
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<a class="reference internal nav-link" href="#week-42-october-17-21">
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Week 42 October 17-21
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</a>
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</li>
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<li class="toc-h3 nav-item toc-entry">
|
||
<a class="reference internal nav-link" href="#week-43-october-24-28">
|
||
Week 43 October 24-28
|
||
</a>
|
||
</li>
|
||
<li class="toc-h3 nav-item toc-entry">
|
||
<a class="reference internal nav-link" href="#week-44-october-31-november-4">
|
||
Week 44 October 31-November 4
|
||
</a>
|
||
</li>
|
||
<li class="toc-h3 nav-item toc-entry">
|
||
<a class="reference internal nav-link" href="#week-45-november-7-11">
|
||
Week 45 November 7-11
|
||
</a>
|
||
</li>
|
||
<li class="toc-h3 nav-item toc-entry">
|
||
<a class="reference internal nav-link" href="#week-46-november-14-18">
|
||
Week 46 November 14-18
|
||
</a>
|
||
</li>
|
||
<li class="toc-h3 nav-item toc-entry">
|
||
<a class="reference internal nav-link" href="#week-47-november-21-25">
|
||
Week 47 November 21-25
|
||
</a>
|
||
</li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
|
||
</nav>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
<div>
|
||
|
||
<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="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>
|
||
<li><p>A selected number of weekly assignments;</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 FYS-STK4155 (Master of Science level) and a FYS-STK3155 (senior undergraduate) course;</p></li>
|
||
<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>
|
||
<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>
|
||
<li><p>CMB: Christopher M. Bishop, Pattern Recognition and Machine Learning</p></li>
|
||
<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>
|
||
<div class="section" id="week-34-august-22-26">
|
||
<h3>Week 34 August 22-26<a class="headerlink" href="#week-34-august-22-26" title="Permalink to this headline">¶</a></h3>
|
||
<ul class="simple">
|
||
<li><p>Lab Wednesday: Introduction to software and repetition of Python Programming</p></li>
|
||
<li><p>Lecture Thursday: Introduction to the course, what is Machine Learning and introduction to Linear Regression.</p></li>
|
||
<li><p>Video of Lecture August 25, 2022 at <a class="reference external" href="https://youtu.be/KL0m3-yhd5w">https://youtu.be/KL0m3-yhd5w</a></p></li>
|
||
<li><p>Lecture Friday: Basics of Linear Regression</p></li>
|
||
<li><p>Video of Lecture August 26, 2022 at <a class="reference external" href="https://youtu.be/ne_xCL2ctM0">https://youtu.be/ne_xCL2ctM0</a></p></li>
|
||
<li><p>Reading recommendations:</p>
|
||
<ul>
|
||
<li><p>Refresh linear algebra, GBC chapters 1 and 2.</p></li>
|
||
<li><p>CMB sections 1.1 and 3.1.</p></li>
|
||
<li><p>HTF chapters 2 and 3.</p></li>
|
||
<li><p>See lecture notes for week 34 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>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</div>
|
||
<div class="section" id="week-35-august-29-september-2">
|
||
<h3>Week 35 August 29-September 2<a class="headerlink" href="#week-35-august-29-september-2" title="Permalink to this headline">¶</a></h3>
|
||
<ul class="simple">
|
||
<li><p>Lab Wednesday: Work on exercises 1-5 for week 35</p></li>
|
||
<li><p>Thursday: Review of ordinary Least Squares with applications, reminder on statistics and start discussion of Ridge Regression and Singular Value Decomposition</p></li>
|
||
<li><p>Video of lecture Thursday at</p></li>
|
||
<li><p>Friday: Discussion of Ridge and Lasso Regression and links with Singular Value Decomposition</p></li>
|
||
<li><p>Video of lecture Friday at</p></li>
|
||
<li><p>Reading recommendations:</p>
|
||
<ul>
|
||
<li><p>See lecture notes for week 35 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>For a review on statistics see jupyter-book <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/statistics.html">https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/statistics.html</a>, the most relevant parts are covered by sections 1.1.1-1.1.5</p></li>
|
||
<li><p>HTF chapter 3. GBC chapters 1 and and sections 3.1-3.11 and 5.1</p></li>
|
||
<li><p>CMB sections 1.1 and 3.1</p></li>
|
||
<li><p>A good review on statistics is given by Murphy’s text, chapter 2, see <a class="reference external" href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/MachineLearningMurphy.pdf">https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/MachineLearningMurphy.pdf</a></p></li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</div>
|
||
<div class="section" id="week-36-september-5-9">
|
||
<h3>Week 36 September 5-9<a class="headerlink" href="#week-36-september-5-9" title="Permalink to this headline">¶</a></h3>
|
||
<ul class="simple">
|
||
<li><p>Lab Wednesday: Exercises 1 and 2 from week 36</p></li>
|
||
<li><p>Lecture Thursday: Summary from last week on SVD, more on Statistics, probability theory and linear regression</p></li>
|
||
<li><p>Video of Lecture</p></li>
|
||
<li><p>Friday: Linear Regression and more links with Statistics, Resampling methods and presentation of first project.</p></li>
|
||
<li><p>Video of Lecture</p></li>
|
||
<li><p>Reading recommendations:</p>
|
||
<ul>
|
||
<li><p>Lectures on Regression for week 36 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>Bishop 1.1, 1.2, 2.1, 2.2, 2.3 and 3.1</p></li>
|
||
<li><p>Hastie et al chapter 3</p></li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</div>
|
||
<div class="section" id="week-37-september-12-16">
|
||
<h3>Week 37 September 12-16<a class="headerlink" href="#week-37-september-12-16" title="Permalink to this headline">¶</a></h3>
|
||
<ul class="simple">
|
||
<li><p>Lab Wednesday: Work on Project 1</p></li>
|
||
<li><p>Lecture Thursday: Resampling methods, cross-validation and Bootstrap</p>
|
||
<ul>
|
||
<li><p>Thursday September: Summary of Ridge and Lasso with examples and start resampling techniques</p>
|
||
<ul>
|
||
<li><p>Video of Lecture</p></li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</li>
|
||
<li><p>Lecture Friday: More on Resampling methods and summary of linear regression</p>
|
||
<ul>
|
||
<li><p>Video of Lecture</p></li>
|
||
</ul>
|
||
</li>
|
||
<li><p>Reading recommendations:</p>
|
||
<ul>
|
||
<li><p>Lectures on Resampling methods for week 37 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>Bishop 1.3 (cross-validation) and 3.2 (bias-variance tradeoff)</p></li>
|
||
<li><p>Hastie et al Chapter 7, here we recommend 7.1-7.5 and 7.10 (cross-validation) and 7.11 (bootstrap)</p></li>
|
||
<li><p>Goodfellow et al discuss some of these topics in sections 5.2-5.5.</p></li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</div>
|
||
<div class="section" id="week-38-september-19-23">
|
||
<h3>Week 38 September 19-23<a class="headerlink" href="#week-38-september-19-23" title="Permalink to this headline">¶</a></h3>
|
||
<ul class="simple">
|
||
<li><p>Lab Wednesday: Work on Project 1</p></li>
|
||
<li><p>Lecture Thursday: Classification problems and Logistic Regression, from binary cases to several categories</p>
|
||
<ul>
|
||
<li><p>Video of Lecture at</p></li>
|
||
</ul>
|
||
</li>
|
||
<li><p>Lecture Friday: Logistic Regression and gradient optimization</p>
|
||
<ul>
|
||
<li><p>Video of Lecture at</p></li>
|
||
</ul>
|
||
</li>
|
||
<li><p>Reading recommendations:</p>
|
||
<ul>
|
||
<li><p>See lecture notes for week 38 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>Bishop 4.1, 4.2 and 4.3. Not all the material is relevant or will be covered. Section 4.3 is the most relevant, but 4.1 and 4.2 give interesting background readings for logistic regression</p></li>
|
||
<li><p>Hastie et al 4.1, 4.2 and 4.3 on logistic regression</p></li>
|
||
<li><p>For a good discussion on gradient methods, see Goodfellow et al section 4.3-4.5 and chapter 8. We will come back to the latter chapter in our discussion of Neural networks as well.</p></li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</div>
|
||
<div class="section" id="week-39-september-26-30">
|
||
<h3>Week 39 September 26-30<a class="headerlink" href="#week-39-september-26-30" title="Permalink to this headline">¶</a></h3>
|
||
<ul class="simple">
|
||
<li><p>Lab Wednesday: Work on Project 1</p></li>
|
||
<li><p>Lecture Thursday: Gradient Optimization methods</p>
|
||
<ul>
|
||
<li><p>Video of Lecture at</p></li>
|
||
</ul>
|
||
</li>
|
||
<li><p>Lecture Friday: Gradient methods</p>
|
||
<ul>
|
||
<li><p>Video of Lecture at</p></li>
|
||
</ul>
|
||
</li>
|
||
<li><p>Reading recommendations:</p>
|
||
<ul>
|
||
<li><p>See lecture notes for week 39 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>For a good discussion on gradient methods, see Goodfellow et al section 4.3-4.5 and chapter 8. We will come back to the latter chapter in our discussion of Neural networks as well.</p></li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</div>
|
||
<div class="section" id="week-40-october-3-7">
|
||
<h3>Week 40 October 3-7<a class="headerlink" href="#week-40-october-3-7" title="Permalink to this headline">¶</a></h3>
|
||
<ul class="simple">
|
||
<li><p>Lab Wednesday: Wrap up project 1</p></li>
|
||
<li><p>Lecture Thursday: Stochastic gradient descent, automatic differentiation and start discussion of feed-forward Neural Network code for regression and classification</p>
|
||
<ul>
|
||
<li><p>Video of Lecture at</p></li>
|
||
</ul>
|
||
</li>
|
||
<li><p>Lecture Friday: Deep Learning and Neural Networks: the back propagation algorithm</p>
|
||
<ul>
|
||
<li><p>Video of Lecture at</p></li>
|
||
</ul>
|
||
</li>
|
||
<li><p>Reading recommendations:</p>
|
||
<ul>
|
||
<li><p>See lecture notes for week 40 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>For neural networks we recommend Goodfellow et al chapters 6 and 7 and Bishop 5.1-5.4</p></li>
|
||
<li><p>For stochastic gradient descent we recommend Goodfellow et al chapter 8</p></li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</div>
|
||
<div class="section" id="week-41-october-10-14">
|
||
<h3>Week 41 October 10-14<a class="headerlink" href="#week-41-october-10-14" title="Permalink to this headline">¶</a></h3>
|
||
<ul class="simple">
|
||
<li><p>Lab Wednesday: Work on project 2</p></li>
|
||
<li><p>Lecture Thursday: Deep learning and Neural Networks, developing a code for Neural Networks</p>
|
||
<ul>
|
||
<li><p>Video of Lecture at <a class="reference external" href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober14.mp4?vrtx=view-as-webpage">https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober14.mp4?vrtx=view-as-webpage</a></p></li>
|
||
</ul>
|
||
</li>
|
||
<li><p>Lecture Friday: Tensorflow and the mathematics of neural network</p>
|
||
<ul>
|
||
<li><p>Video of Lecture at <a class="reference external" href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureOctober15.mp4?vrtx=view-as-webpage">https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureOctober15.mp4?vrtx=view-as-webpage</a></p></li>
|
||
</ul>
|
||
</li>
|
||
<li><p>Reading recommendations:</p>
|
||
<ul>
|
||
<li><p>See lecture notes for week 41 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>For neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. chapter 11 and 12 on practicalities and applications</p></li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</div>
|
||
<div class="section" id="week-42-october-17-21">
|
||
<h3>Week 42 October 17-21<a class="headerlink" href="#week-42-october-17-21" title="Permalink to this headline">¶</a></h3>
|
||
<ul class="simple">
|
||
<li><p>Lab Wednesday: Work on project 2</p></li>
|
||
<li><p>Lecture Thursday: Solving differential equations with neural networks and start Convolutional Neural Networks and classification problems</p>
|
||
<ul>
|
||
<li><p>Video of Lecture at</p></li>
|
||
</ul>
|
||
</li>
|
||
<li><p>Lecture Friday: Convolutional Neural Networks and classification problems</p>
|
||
<ul>
|
||
<li><p>Video of Lecture at</p></li>
|
||
</ul>
|
||
</li>
|
||
<li><p>Reading recommendations:</p>
|
||
<ul>
|
||
<li><p>See lecture notes for week 42 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>For neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. See also chapter 11 and 12 on practicalities and applications</p></li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</div>
|
||
<div class="section" id="week-43-october-24-28">
|
||
<h3>Week 43 October 24-28<a class="headerlink" href="#week-43-october-24-28" title="Permalink to this headline">¶</a></h3>
|
||
<ul class="simple">
|
||
<li><p>Lab Wednesday: Work on project 2</p></li>
|
||
<li><p>Lecture Thursday: Recurrent Neural Networks</p>
|
||
<ul>
|
||
<li><p>Video of Lecture at</p></li>
|
||
</ul>
|
||
</li>
|
||
<li><p>Lecture Friday: Recurrent Neural Networks and time series and principal component analysis (PCA)</p>
|
||
<ul>
|
||
<li><p>Video of Lecture at</p></li>
|
||
</ul>
|
||
</li>
|
||
<li><p>Reading recommendations:</p>
|
||
<ul>
|
||
<li><p>See lecture notes for week 43 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>For RNNs, see Goodfellow et al chapter 10 and discussions in chapter 11 and 12 on practicalities and applications</p></li>
|
||
<li><p>For PCA, see lecture notes chapter 11 and Geron’s text chapter 8</p></li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</div>
|
||
<div class="section" id="week-44-october-31-november-4">
|
||
<h3>Week 44 October 31-November 4<a class="headerlink" href="#week-44-october-31-november-4" title="Permalink to this headline">¶</a></h3>
|
||
<ul class="simple">
|
||
<li><p>Lab Wednesday: Work on project 2</p></li>
|
||
<li><p>Lecture Thursday: Summary on PCA and discussion of Clustering for unsupervised learning. Decision trees, classification and regression</p>
|
||
<ul>
|
||
<li><p>Video of Lecture at</p></li>
|
||
</ul>
|
||
</li>
|
||
<li><p>Lecture Friday: Decision trees, basic algorithms</p>
|
||
<ul>
|
||
<li><p>Video of Lecture at</p></li>
|
||
</ul>
|
||
</li>
|
||
<li><p>Reading recommendations:</p>
|
||
<ul>
|
||
<li><p>See lecture notes for week 44 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>Hastie et al sections 9.1 and 9.2. Geron’s text chapter 6 (Decision trees) and chapter 8 on PCA and Clustering</p></li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</div>
|
||
<div class="section" id="week-45-november-7-11">
|
||
<h3>Week 45 November 7-11<a class="headerlink" href="#week-45-november-7-11" title="Permalink to this headline">¶</a></h3>
|
||
<ul class="simple">
|
||
<li><p>Lab Wednesday: Work on project 2, project 3 available.</p></li>
|
||
<li><p>Lecture Thursday: Ensemble methods, bagging and random forests</p>
|
||
<ul>
|
||
<li><p>Video of Lecture at</p></li>
|
||
</ul>
|
||
</li>
|
||
<li><p>Lecture Friday: Boosting and gradient boosting</p>
|
||
<ul>
|
||
<li><p>Video of Lecture at</p></li>
|
||
</ul>
|
||
</li>
|
||
<li><p>Reading recommendations:</p>
|
||
<ul>
|
||
<li><p>See lecture notes for week 45 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>Hastie et al chapter 10</p></li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</div>
|
||
<div class="section" id="week-46-november-14-18">
|
||
<h3>Week 46 November 14-18<a class="headerlink" href="#week-46-november-14-18" title="Permalink to this headline">¶</a></h3>
|
||
<ul class="simple">
|
||
<li><p>Lab Wednesday: Work on project 3</p></li>
|
||
<li><p>Lecture Thursday: Support Vector machines</p></li>
|
||
<li><p>Lecture Friday: Workshop on project 3</p>
|
||
<ul>
|
||
<li><p>Video of Lecture at</p></li>
|
||
</ul>
|
||
</li>
|
||
<li><p>Reading recommendations:</p>
|
||
<ul>
|
||
<li><p>See lecture notes for week 46 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>Hastie et al chapter 12</p></li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</div>
|
||
<div class="section" id="week-47-november-21-25">
|
||
<h3>Week 47 November 21-25<a class="headerlink" href="#week-47-november-21-25" title="Permalink to this headline">¶</a></h3>
|
||
<ul class="simple">
|
||
<li><p>Lab Wednesday: Work on project 3</p></li>
|
||
<li><p>Lecture Thursday: Support Vector Machines</p>
|
||
<ul>
|
||
<li><p>Video of Lecture</p></li>
|
||
</ul>
|
||
</li>
|
||
<li><p>Lecture Friday: Support Vector Machines and Summary of course</p>
|
||
<ul>
|
||
<li><p>Video of Lecture</p></li>
|
||
</ul>
|
||
</li>
|
||
<li><p>Reading recommendations:</p>
|
||
<ul>
|
||
<li><p>See lecture notes for week 47 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>Geron’s chapter 5.</p></li>
|
||
<li><p>Hastie et al Chapter 12 (sections 12.1-12.3 are the most relevant ones)</p></li>
|
||
<li><p>Bishop chapter 7, with sections 7.1 and 7.2 as the essential ones</p></li>
|
||
</ul>
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