Morten Hjorth-Jensen
455 lines
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455 lines
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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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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 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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</ul>
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<p aria-level="2" class="caption" role="heading">
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From Regression to Support Vector Machines
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3. Linear Regression
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</a>
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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 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 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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</ul>
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<p aria-level="2" class="caption" role="heading">
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Decision Trees, Ensemble Methods and Boosting
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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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<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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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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15. Solving Differential Equations with Deep Learning
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16. Convolutional Neural Networks
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17. Recurrent neural networks: Overarching view
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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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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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Exercises week 42
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Week 42 Constructing a Neural Network code with examples
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Exercises Week 42: Logistic Regression and Optimization, reminders from week 38 and week 40
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Projects
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Project 1 on Machine Learning, deadline October 7 (midnight), 2024
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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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By Morten Hjorth-Jensen<br/>
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© Copyright 2021.<br/>
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