update p3

doc/Projects/2022/Project3/html/Project3.html

@@ -143,11 +143,11 @@ div.toc p,a {
                None,
                'part-d-solving-eigenvalue-problems'),
               ('Part e)', 3, None, 'part-e'),
-              ('Additonal (optional) exercise, adding 30 more points to final '
+              ('Additonal (optional) exercise, adding 20 more points to final '
                'score',
                2,
                None,
-               'additonal-optional-exercise-adding-30-more-points-to-final-score'),
+               'additonal-optional-exercise-adding-20-more-points-to-final-score'),
               ('Introduction to numerical projects',
                2,
                None,
@@ -194,7 +194,7 @@ MathJax.Hub.Config({
 </center>
 <br>
 <center>
-<h4>Nov 14, 2022</h4>
+<h4>Nov 15, 2022</h4>
 </center> <!-- date -->
 <br>
 <h1 id="paths-for-project-3">Paths for project 3  </h1>
@@ -206,9 +206,11 @@ MathJax.Hub.Config({
 <li> The <a href="https://archive.ics.uci.edu/ml/index.php" target="_blank">University of California at Irvine (UCI) with its  machine learning repository</a>.</li>
 <li> Or other sources.</li>
 </ol>
+<p>As an example on applications of the methods we have discussed in this course, see the article from economy that deals with machine learning on bankruptcy data from Spain at URL":"https://link.springer.com/article/10.1007/s10614-020-10078-2". The data are most likely not accessible within short time, but it is a nice demonstration on how to use the various machine learning methods we have discussed during the semester.</p>
+
 <p>The approach to the analysis of these new data sets should follow to a large extent what you did in projects 1 and 2. That is:</p>
 <ol>
-<li> Whether you end up with a regression or a classification problem, you should employ at least two of the methods we have discussed among <b>linear regression (including Ridge and Lasso)</b>, <b>Logistic Regression</b>, <b>Neural Networks</b>, <b>Convolution Neural Networks</b>, <b>Recurrent Neural Networks</b>, <b>Adversarial Neural Networks</b>, <b>Support Vector Machines</b> and <b>Decision Trees, Random Forests, Bagging and Boosting</b>. You could for example explore  all of the approaches from decision trees, via bagging and voting classifiers, to random forests, boosting and finally XGboost. If you wish to venture into <b>convolutional neural networks</b> or <b>recurrent neural networks</b>, or extensions of neural networkds, feel free to do so. You can also study unsupervised methods, although we in this course have mainly paid attendtion to supervised learning. The methods we have explored as <b>Principal Component Analysis</b> and <b>k-means Clustering</b>.</li>
+<li> Whether you end up with a regression or a classification problem, you should employ at least two of the methods we have discussed among <b>linear regression (including Ridge and Lasso)</b>, <b>Logistic Regression</b>, <b>Neural Networks</b>, <b>Convolution Neural Networks</b>, <b>Recurrent Neural Networks</b>, <b>Adversarial Neural Networks</b>, <b>Support Vector Machines</b> and <b>Decision Trees, Random Forests, Bagging and Boosting</b>. You could for example explore  all of the approaches from decision trees, via bagging and voting classifiers, to random forests, boosting and finally XGboost. If you wish to venture into <b>convolutional neural networks</b> or <b>recurrent neural networks</b>, or extensions of neural networkds, feel free to do so. You can also study unsupervised methods, although we in this course have mainly paid attention to supervised learning. We will only touch upon unsupervised methods like  <b>Principal Component Analysis</b> and <b>k-means Clustering</b>.</li>
 </ol>
 <p>For Boosting, feel also free to write your own codes.</p>
 
@@ -225,7 +227,7 @@ MathJax.Hub.Config({
 
 <p>This is a field with a large interest recently, spanning from studies of turbulence in fluid mechanics and meteorology to the solution of quantum mechanical systems. As reading background you can use the slides <a href="https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html" target="_blank">from week 42</a> and/or the textbook by <a href="https://www.springer.com/gp/book/9789401798150" target="_blank">Yadav et al</a>.</p>
 
-<p><b>Note</b>: Project 3 has an additional exercise which can give you an additional score of 30 (thirty) points. These are added to the total score from all projects. See below for the additional exercise. </p>
+<p><b>Note</b>: Project 3 has an additional exercise which can give you an additional score of 20 (twenty) points. These are added to the total score from all projects. See below for the additional exercise. </p>
 <h2 id="the-basic-structure-of-your-project">The basic structure of your project </h2>
 
 <p>Here follows a set up on how to structure your report and analyze the data you have opted for.</p>
@@ -361,7 +363,7 @@ eigenvalues. Compare with the solution from numerical diagonalization with stand
 <h3 id="part-e">Part e) </h3>
 
 <p>Finally, present a critical assessment of the methods you have studied and discuss the potential for the solving differential equations and eigenvalue problems with machine learning methods. </p>
-<h2 id="additonal-optional-exercise-adding-30-more-points-to-final-score">Additonal (optional) exercise, adding 30 more points to final score </h2>
+<h2 id="additonal-optional-exercise-adding-20-more-points-to-final-score">Additonal (optional) exercise, adding 20 more points to final score </h2>
 
 <p>This exercise can be done independently of the other tasks. Here you
 can also choose the data set you want to use. Furthermore, you can use