Morten Hjorth-Jensen
516 lines
18 KiB
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516 lines
18 KiB
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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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Teachers and Grading
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Textbooks
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Review of Statistics with Resampling Techniques and Linear Algebra
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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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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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9. Decision trees, overarching aims
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</a>
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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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11. Basic ideas of the Principal Component Analysis (PCA)
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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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<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 class="reference internal" href="chapter12.html">
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16. Convolutional Neural Networks
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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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</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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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 class="reference internal" href="exercisesweek37.html">
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Exercises week 37
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Week 37: Statistical interpretations and Resampling Methods
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<a class="reference internal" href="exercisesweek38.html">
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Exercises week 38
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Week 38: Logistic Regression and Optimization
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<a class="reference internal" href="exercisesweek39.html">
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Exercises week 39
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<a class="reference internal" href="week39.html">
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Week 39: Optimization and Gradient Methods
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</ul>
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<p aria-level="2" class="caption" role="heading">
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Projects
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Project 1 on Machine Learning, deadline October 7 (midnight), 2024
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Weekly Schedule
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<h1>Teaching schedule with links to material</h1>
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Weekly Schedule
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<div class="tex2jax_ignore mathjax_ignore section" 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 headline">¶</a></h1>
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<p>This course will be delivered in a hybrid mode, with online lectures and on site or online laboratory sessions.</p>
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<ol class="simple">
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<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>
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<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>
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<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>
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<li><p>A selected number of weekly assignments;</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 FYS-STK4155 (Master of Science level) and a FYS-STK3155 (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 emails 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="weekly-schedule">
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<h2>Weekly Schedule<a class="headerlink" href="#weekly-schedule" title="Permalink to this headline">¶</a></h2>
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<p>For the reading assignments we use the following abbreviations:</p>
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<ul class="simple">
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<li><p>GBC: Goodfellow, Bengio, and Courville, Deep Learning</p></li>
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<li><p>CMB: Christopher M. Bishop, Pattern Recognition and Machine Learning</p></li>
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<li><p>HTF: Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning</p></li>
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