Morten Hjorth-Jensen
656 lines
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Applied Data Analysis and Machine Learning
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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">About the course</span></p>
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<li class="toctree-l1"><a class="reference internal" href="schedule.html">Teaching schedule with links to material</a></li>
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</ul>
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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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<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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</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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<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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</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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<ul class="nav bd-sidenav">
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<li class="toctree-l1"><a class="reference internal" href="chapter6.html">9. Decision trees, overarching aims</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapter7.html">10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods</a></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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<ul class="nav bd-sidenav">
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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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</ul>
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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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<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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</ul>
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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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<ul class="current nav bd-sidenav">
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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="week34.html">Week 34: Introduction to the course, Logistics and Practicalities</a></li>
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek35.html">Exercises week 35</a></li>
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<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>
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<li class="toctree-l1"><a class="reference internal" href="week36.html">Week 36: Linear Regression and Statistical interpretations</a></li>
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek37.html">Exercises week 37</a></li>
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<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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<li class="toctree-l1"><a class="reference internal" href="exercisesweek39.html">Exercises week 39</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week39.html">Week 39: Optimization and Gradient Methods</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek41.html">Exercises week 41</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek42.html">Exercises week 42</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week42.html">Week 42 Constructing a Neural Network code with examples</a></li>
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<li class="toctree-l1"><a class="reference internal" href="additionweek42.html">Exercises Week 42: Logistic Regression and Optimization, reminders from week 38 and week 40</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week43.html">Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations</a></li>
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek43.html">Exercises week 43</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week44.html">Week 44, Convolutional Neural Networks (CNN)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week45.html">Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week46.html">Week 46: Decision Trees, Ensemble methods and Random Forests</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week47.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods</a></li>
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<li class="toctree-l1 current active"><a class="current reference internal" href="#">Exercise week 47</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
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<li class="toctree-l1"><a class="reference internal" href="project1.html">Project 1 on Machine Learning, deadline October 7 (midnight), 2024</a></li>
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<li class="toctree-l1"><a class="reference internal" href="project2.html">Project 2 on Machine Learning, deadline November 4 (Midnight)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="project3.html">Project 3 on Machine Learning, deadline December 9 (midnight), 2024</a></li>
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<h1>Exercise week 47</h1>
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<h2> Contents </h2>
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<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">Exercise week 47</a></li>
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<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#overarching-aims-of-the-exercises-this-week">Overarching aims of the exercises this week</a><ul class="visible nav section-nav flex-column">
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-1-linear-and-logistic-regression-methods">Exercise 1: Linear and logistic regression methods</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-2-deep-learning">Exercise 2: Deep learning</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-3-decision-trees-and-ensemble-methods">Exercise 3: Decision trees and ensemble methods</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-4-optimization-part">Exercise 4: Optimization part</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-5-analysis-of-results">Exercise 5: Analysis of results</a></li>
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<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)
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doconce format html exercisesweek47.do.txt -->
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<!-- dom:TITLE: Exercise week 47 --><section class="tex2jax_ignore mathjax_ignore" id="exercise-week-47">
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<h1>Exercise week 47<a class="headerlink" href="#exercise-week-47" title="Link to this heading">#</a></h1>
|
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<p><strong>November 18-22, 2024</strong></p>
|
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<p>Date: <strong>Deadline is Friday November 22 at midnight</strong></p>
|
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</section>
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<section class="tex2jax_ignore mathjax_ignore" id="overarching-aims-of-the-exercises-this-week">
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<h1>Overarching aims of the exercises this week<a class="headerlink" href="#overarching-aims-of-the-exercises-this-week" title="Link to this heading">#</a></h1>
|
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<p>The exercise set this week is meant as a summary of many of the
|
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central elements in various machine learning algorithms, with a slight
|
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bias towards deep learning methods and their training. You don’t need to answer all questions.</p>
|
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<p>The last weekly exercise (week 48) is a general course survey.</p>
|
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<section id="exercise-1-linear-and-logistic-regression-methods">
|
||
<h2>Exercise 1: Linear and logistic regression methods<a class="headerlink" href="#exercise-1-linear-and-logistic-regression-methods" title="Link to this heading">#</a></h2>
|
||
<ol class="arabic simple">
|
||
<li><p>What is the main difference between ordinary least squares and Ridge regression?</p></li>
|
||
<li><p>Which kind of data set would you use logistic regression for?</p></li>
|
||
<li><p>In linear regression you assume that your output is described by a continuous non-stochastic function <span class="math notranslate nohighlight">\(f(x)\)</span>. Which is the equivalent function in logistic regression?</p></li>
|
||
<li><p>Can you find an analytic solution to a logistic regression type of problem?</p></li>
|
||
<li><p>What kind of cost function would you use in logistic regression?</p></li>
|
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</ol>
|
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</section>
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<section id="exercise-2-deep-learning">
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<h2>Exercise 2: Deep learning<a class="headerlink" href="#exercise-2-deep-learning" title="Link to this heading">#</a></h2>
|
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<ol class="arabic simple">
|
||
<li><p>What is an activation function and discuss the use of an activation function? Explain three different types of activation functions?</p></li>
|
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<li><p>Describe the architecture of a typical feed forward Neural Network (NN).</p></li>
|
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<li><p>You are using a deep neural network for a prediction task. After training your model, you notice that it is strongly overfitting the training set and that the performance on the test isn’t good. What can you do to reduce overfitting?</p></li>
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<li><p>How would you know if your model is suffering from the problem of exploding Gradients?</p></li>
|
||
<li><p>Can you name and explain a few hyperparameters used for training a neural network?</p></li>
|
||
<li><p>Describe the architecture of a typical Convolutional Neural Network (CNN)</p></li>
|
||
<li><p>What is the vanishing gradient problem in Neural Networks and how to fix it?</p></li>
|
||
<li><p>When it comes to training an artificial neural network, what could the reason be for why the cost/loss doesn’t decrease in a few epochs?</p></li>
|
||
<li><p>How does L1/L2 regularization affect a neural network?</p></li>
|
||
<li><p>What is(are) the advantage(s) of deep learning over traditional methods like linear regression or logistic regression?</p></li>
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</ol>
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</section>
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<section id="exercise-3-decision-trees-and-ensemble-methods">
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<h2>Exercise 3: Decision trees and ensemble methods<a class="headerlink" href="#exercise-3-decision-trees-and-ensemble-methods" title="Link to this heading">#</a></h2>
|
||
<ol class="arabic simple">
|
||
<li><p>Mention some pros and cons when using decision trees</p></li>
|
||
<li><p>How do we grow a tree? And which are the main parameters?</p></li>
|
||
<li><p>Mention some of the benefits with using ensemble methods (like bagging, random forests and boosting methods)?</p></li>
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||
<li><p>Why would you prefer a random forest instead of using Bagging to grow a forest?</p></li>
|
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<li><p>What is the basic philosophy behind boosting methods?</p></li>
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</ol>
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</section>
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<section id="exercise-4-optimization-part">
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<h2>Exercise 4: Optimization part<a class="headerlink" href="#exercise-4-optimization-part" title="Link to this heading">#</a></h2>
|
||
<ol class="arabic simple">
|
||
<li><p>Which is the basic mathematical root-finding method behind essentially all gradient descent approaches(stochastic and non-stochastic)?</p></li>
|
||
<li><p>And why don’t we use it? Or stated differently, why do we introduce the learning rate as a parameter?</p></li>
|
||
<li><p>What might happen if you set the momentum hyperparameter too close to 1 (e.g., 0.9999) when using an optimizer for the learning rate?</p></li>
|
||
<li><p>Why should we use stochastic gradient descent instead of plain gradient descent?</p></li>
|
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<li><p>Which parameters would you need to tune when use a stochastic gradient descent approach?</p></li>
|
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</ol>
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</section>
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<section id="exercise-5-analysis-of-results">
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<h2>Exercise 5: Analysis of results<a class="headerlink" href="#exercise-5-analysis-of-results" title="Link to this heading">#</a></h2>
|
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<ol class="arabic simple">
|
||
<li><p>How do you assess overfitting and underfitting?</p></li>
|
||
<li><p>Why do we divide the data in test and train and/or eventually validation sets?</p></li>
|
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<li><p>Why would you use resampling methods in the data analysis? Mention some widely popular resampling methods.</p></li>
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<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">Exercise week 47</a></li>
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<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#overarching-aims-of-the-exercises-this-week">Overarching aims of the exercises this week</a><ul class="visible nav section-nav flex-column">
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-1-linear-and-logistic-regression-methods">Exercise 1: Linear and logistic regression methods</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-2-deep-learning">Exercise 2: Deep learning</a></li>
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