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.
+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:
For Boosting, feel also free to write your own codes.
@@ -194,7 +196,7 @@ MathJax.Hub.Config({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 from week 42 and/or the textbook by Yadav et al.
-Note: 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.
+Note: 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.
Here follows a set up on how to structure your report and analyze the data you have opted for.
@@ -330,7 +332,7 @@ eigenvalues. Compare with the solution from numerical diagonalization with standFinally, 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.
-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 diff --git a/doc/Projects/2022/Project3/html/Project3-bs.html b/doc/Projects/2022/Project3/html/Project3-bs.html index 6a86b4453..119db48b5 100644 --- a/doc/Projects/2022/Project3/html/Project3-bs.html +++ b/doc/Projects/2022/Project3/html/Project3-bs.html @@ -65,11 +65,11 @@ doconce format html Project3.do.txt --html_style=bootstrap --pygments_html_style 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, @@ -130,7 +130,7 @@ MathJax.Hub.Config({
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.
+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:
For Boosting, feel also free to write your own codes.
@@ -194,7 +196,7 @@ MathJax.Hub.Config({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 from week 42 and/or the textbook by Yadav et al.
-Note: 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.
+Note: 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.
Here follows a set up on how to structure your report and analyze the data you have opted for.
@@ -330,7 +332,7 @@ eigenvalues. Compare with the solution from numerical diagonalization with standFinally, 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.
-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
diff --git a/doc/Projects/2022/Project3/html/Project3.html b/doc/Projects/2022/Project3/html/Project3.html
index 0ce8a7793..7957b5e9e 100644
--- a/doc/Projects/2022/Project3/html/Project3.html
+++ b/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({
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.
+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:
For Boosting, feel also free to write your own codes.
@@ -225,7 +227,7 @@ MathJax.Hub.Config({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 from week 42 and/or the textbook by Yadav et al.
-Note: 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.
+Note: 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.
Here follows a set up on how to structure your report and analyze the data you have opted for.
@@ -361,7 +363,7 @@ eigenvalues. Compare with the solution from numerical diagonalization with standFinally, 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.
-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 diff --git a/doc/Projects/2022/Project3/ipynb/Project3.ipynb b/doc/Projects/2022/Project3/ipynb/Project3.ipynb index 996f643d5..06aa75f6d 100644 --- a/doc/Projects/2022/Project3/ipynb/Project3.ipynb +++ b/doc/Projects/2022/Project3/ipynb/Project3.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "0c2e93c2", + "id": "4266c21c", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "2dc0413d", + "id": "403dffc3", "metadata": { "editable": true }, @@ -22,14 +22,14 @@ "# Project 3 on Machine Learning, deadline December 15 (midnight), 2021\n", "**[Data Analysis and Machine Learning FYS-STK3155/FYS4155](http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html)**, Department of Physics, University of Oslo, Norway\n", "\n", - "Date: **Nov 14, 2022**\n", + "Date: **Nov 15, 2022**\n", "\n", "Copyright 1999-2022, [Data Analysis and Machine Learning FYS-STK3155/FYS4155](http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html). Released under CC Attribution-NonCommercial 4.0 license" ] }, { "cell_type": "markdown", - "id": "92237bb9", + "id": "e8360cac", "metadata": { "editable": true }, @@ -39,7 +39,7 @@ }, { "cell_type": "markdown", - "id": "9b6ca5fb", + "id": "1bf77954", "metadata": { "editable": true }, @@ -53,8 +53,10 @@ "\n", "3. Or other sources.\n", "\n", + "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.\n", + "\n", "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:\n", - "1. Whether you end up with a regression or a classification problem, you should employ at least two of the methods we have discussed among **linear regression (including Ridge and Lasso)**, **Logistic Regression**, **Neural Networks**, **Convolution Neural Networks**, **Recurrent Neural Networks**, **Adversarial Neural Networks**, **Support Vector Machines** and **Decision Trees, Random Forests, Bagging and Boosting**. 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 **convolutional neural networks** or **recurrent neural networks**, 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 **Principal Component Analysis** and **k-means Clustering**.\n", + "1. Whether you end up with a regression or a classification problem, you should employ at least two of the methods we have discussed among **linear regression (including Ridge and Lasso)**, **Logistic Regression**, **Neural Networks**, **Convolution Neural Networks**, **Recurrent Neural Networks**, **Adversarial Neural Networks**, **Support Vector Machines** and **Decision Trees, Random Forests, Bagging and Boosting**. 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 **convolutional neural networks** or **recurrent neural networks**, 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 **Principal Component Analysis** and **k-means Clustering**.\n", "\n", "For Boosting, feel also free to write your own codes.\n", "\n", @@ -74,12 +76,12 @@ "\n", "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 [from week 42](https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html) and/or the textbook by [Yadav et al](https://www.springer.com/gp/book/9789401798150).\n", "\n", - "**Note**: 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." + "**Note**: 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." ] }, { "cell_type": "markdown", - "id": "d3d26ef5", + "id": "b983bd08", "metadata": { "editable": true }, @@ -91,7 +93,7 @@ }, { "cell_type": "markdown", - "id": "a03830d7", + "id": "deecb4e5", "metadata": { "editable": true }, @@ -103,7 +105,7 @@ }, { "cell_type": "markdown", - "id": "3bf25ce5", + "id": "1e46b462", "metadata": { "editable": true }, @@ -115,7 +117,7 @@ }, { "cell_type": "markdown", - "id": "0ba7777e", + "id": "cd593738", "metadata": { "editable": true }, @@ -127,7 +129,7 @@ }, { "cell_type": "markdown", - "id": "129fff02", + "id": "8e82b99f", "metadata": { "editable": true }, @@ -139,7 +141,7 @@ }, { "cell_type": "markdown", - "id": "da24bb44", + "id": "531cb90c", "metadata": { "editable": true }, @@ -151,7 +153,7 @@ }, { "cell_type": "markdown", - "id": "b4d8bd6f", + "id": "df454e93", "metadata": { "editable": true }, @@ -171,7 +173,7 @@ }, { "cell_type": "markdown", - "id": "9fb8b554", + "id": "1891abfd", "metadata": { "editable": true }, @@ -185,7 +187,7 @@ }, { "cell_type": "markdown", - "id": "9c1a12d5", + "id": "805bfc54", "metadata": { "editable": true }, @@ -197,7 +199,7 @@ }, { "cell_type": "markdown", - "id": "81df663e", + "id": "04191794", "metadata": { "editable": true }, @@ -207,7 +209,7 @@ }, { "cell_type": "markdown", - "id": "3d639530", + "id": "48917dfc", "metadata": { "editable": true }, @@ -219,7 +221,7 @@ }, { "cell_type": "markdown", - "id": "b4a3d10e", + "id": "62431c9b", "metadata": { "editable": true }, @@ -229,7 +231,7 @@ }, { "cell_type": "markdown", - "id": "e72fff12", + "id": "eccc5397", "metadata": { "editable": true }, @@ -241,7 +243,7 @@ }, { "cell_type": "markdown", - "id": "1448ffdc", + "id": "bef3bdae", "metadata": { "editable": true }, @@ -252,7 +254,7 @@ }, { "cell_type": "markdown", - "id": "4a10dc7f", + "id": "1f76902d", "metadata": { "editable": true }, @@ -264,7 +266,7 @@ }, { "cell_type": "markdown", - "id": "665a89f8", + "id": "a3ca949c", "metadata": { "editable": true }, @@ -274,7 +276,7 @@ }, { "cell_type": "markdown", - "id": "bfb1865c", + "id": "28a0daf3", "metadata": { "editable": true }, @@ -286,7 +288,7 @@ }, { "cell_type": "markdown", - "id": "caac2a80", + "id": "856db5f5", "metadata": { "editable": true }, @@ -299,7 +301,7 @@ }, { "cell_type": "markdown", - "id": "864c151d", + "id": "74f847c2", "metadata": { "editable": true }, @@ -311,7 +313,7 @@ }, { "cell_type": "markdown", - "id": "0e7ee4f9", + "id": "50c6a638", "metadata": { "editable": true }, @@ -321,7 +323,7 @@ }, { "cell_type": "markdown", - "id": "e40b01ba", + "id": "732fa78e", "metadata": { "editable": true }, @@ -333,7 +335,7 @@ }, { "cell_type": "markdown", - "id": "bde8abf8", + "id": "83b8bd01", "metadata": { "editable": true }, @@ -343,7 +345,7 @@ }, { "cell_type": "markdown", - "id": "fbb28a61", + "id": "46d68c66", "metadata": { "editable": true }, @@ -355,7 +357,7 @@ }, { "cell_type": "markdown", - "id": "88fdfd23", + "id": "423e0b70", "metadata": { "editable": true }, @@ -366,7 +368,7 @@ }, { "cell_type": "markdown", - "id": "ae0f5018", + "id": "9416896f", "metadata": { "editable": true }, @@ -382,7 +384,7 @@ }, { "cell_type": "markdown", - "id": "64e90ef9", + "id": "9484df9f", "metadata": { "editable": true }, @@ -399,7 +401,7 @@ }, { "cell_type": "markdown", - "id": "ea02a377", + "id": "dbfc73ef", "metadata": { "editable": true }, @@ -415,7 +417,7 @@ }, { "cell_type": "markdown", - "id": "57f27bd3", + "id": "ea92bb58", "metadata": { "editable": true }, @@ -427,12 +429,12 @@ }, { "cell_type": "markdown", - "id": "684f4cd8", + "id": "2f3ea4b0", "metadata": { "editable": true }, "source": [ - "## Additonal (optional) exercise, adding 30 more points to final score\n", + "## Additonal (optional) exercise, adding 20 more points to final score\n", "\n", "This exercise can be done independently of the other tasks. Here you\n", "can also choose the data set you want to use. Furthermore, you can use\n", @@ -462,7 +464,7 @@ }, { "cell_type": "markdown", - "id": "2a6962e8", + "id": "8bfdf8aa", "metadata": { "editable": true }, @@ -493,7 +495,7 @@ }, { "cell_type": "markdown", - "id": "4c047e8f", + "id": "b99c4ae5", "metadata": { "editable": true }, @@ -515,7 +517,7 @@ }, { "cell_type": "markdown", - "id": "d7750944", + "id": "7a32c59b", "metadata": { "editable": true }, diff --git a/doc/Projects/2022/Project3/ipynb/ipynb-Project3-src.tar.gz b/doc/Projects/2022/Project3/ipynb/ipynb-Project3-src.tar.gz index 5f3a85de0..eefbedad3 100644 Binary files a/doc/Projects/2022/Project3/ipynb/ipynb-Project3-src.tar.gz and b/doc/Projects/2022/Project3/ipynb/ipynb-Project3-src.tar.gz differ diff --git a/doc/Projects/2022/Project3/pdf/Project3.p.tex b/doc/Projects/2022/Project3/pdf/Project3.p.tex index 1e2a16adf..3fbc9935d 100644 --- a/doc/Projects/2022/Project3/pdf/Project3.p.tex +++ b/doc/Projects/2022/Project3/pdf/Project3.p.tex @@ -149,7 +149,7 @@ Project 3 on Machine Learning, deadline December 15 (midnight), 2021 % --- begin date --- \begin{center} -Nov 14, 2022 +Nov 15, 2022 \end{center} % --- end date --- @@ -170,9 +170,11 @@ For project 3, you can propose own data sets that relate to your research intere \end{enumerate} \noindent +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. + 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: \begin{enumerate} -\item Whether you end up with a regression or a classification problem, you should employ at least two of the methods we have discussed among \textbf{linear regression (including Ridge and Lasso)}, \textbf{Logistic Regression}, \textbf{Neural Networks}, \textbf{Convolution Neural Networks}, \textbf{Recurrent Neural Networks}, \textbf{Adversarial Neural Networks}, \textbf{Support Vector Machines} and \textbf{Decision Trees, Random Forests, Bagging and Boosting}. 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 \textbf{convolutional neural networks} or \textbf{recurrent neural networks}, 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 \textbf{Principal Component Analysis} and \textbf{k-means Clustering}. +\item Whether you end up with a regression or a classification problem, you should employ at least two of the methods we have discussed among \textbf{linear regression (including Ridge and Lasso)}, \textbf{Logistic Regression}, \textbf{Neural Networks}, \textbf{Convolution Neural Networks}, \textbf{Recurrent Neural Networks}, \textbf{Adversarial Neural Networks}, \textbf{Support Vector Machines} and \textbf{Decision Trees, Random Forests, Bagging and Boosting}. 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 \textbf{convolutional neural networks} or \textbf{recurrent neural networks}, 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 \textbf{Principal Component Analysis} and \textbf{k-means Clustering}. \end{enumerate} \noindent @@ -197,7 +199,7 @@ We propose also an alternative to the above. This is a project on using machine 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 \href{{https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html}}{from week 42} and/or the textbook by \href{{https://www.springer.com/gp/book/9789401798150}}{Yadav et al}. -\textbf{Note}: 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. +\textbf{Note}: 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. \subsection{The basic structure of your project} @@ -304,7 +306,7 @@ eigenvalues. Compare with the solution from numerical diagonalization with stand \paragraph{Part e).} 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. -\subsection{Additonal (optional) exercise, adding 30 more points to final score} +\subsection{Additonal (optional) exercise, adding 20 more points to final score} 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 diff --git a/doc/Projects/2022/Project3/pdf/Project3.pdf b/doc/Projects/2022/Project3/pdf/Project3.pdf index 408fa8737..1cb0e54bb 100644 Binary files a/doc/Projects/2022/Project3/pdf/Project3.pdf and b/doc/Projects/2022/Project3/pdf/Project3.pdf differ diff --git a/doc/Projects/2022/Project3/pdf/Project3.tex b/doc/Projects/2022/Project3/pdf/Project3.tex index f29685e09..aeed32dd9 100644 --- a/doc/Projects/2022/Project3/pdf/Project3.tex +++ b/doc/Projects/2022/Project3/pdf/Project3.tex @@ -123,7 +123,7 @@ Project 3 on Machine Learning, deadline December 15 (midnight), 2021 % --- begin date --- \begin{center} -Nov 14, 2022 +Nov 15, 2022 \end{center} % --- end date --- @@ -144,9 +144,11 @@ For project 3, you can propose own data sets that relate to your research intere \end{enumerate} \noindent +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. + 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: \begin{enumerate} -\item Whether you end up with a regression or a classification problem, you should employ at least two of the methods we have discussed among \textbf{linear regression (including Ridge and Lasso)}, \textbf{Logistic Regression}, \textbf{Neural Networks}, \textbf{Convolution Neural Networks}, \textbf{Recurrent Neural Networks}, \textbf{Adversarial Neural Networks}, \textbf{Support Vector Machines} and \textbf{Decision Trees, Random Forests, Bagging and Boosting}. 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 \textbf{convolutional neural networks} or \textbf{recurrent neural networks}, 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 \textbf{Principal Component Analysis} and \textbf{k-means Clustering}. +\item Whether you end up with a regression or a classification problem, you should employ at least two of the methods we have discussed among \textbf{linear regression (including Ridge and Lasso)}, \textbf{Logistic Regression}, \textbf{Neural Networks}, \textbf{Convolution Neural Networks}, \textbf{Recurrent Neural Networks}, \textbf{Adversarial Neural Networks}, \textbf{Support Vector Machines} and \textbf{Decision Trees, Random Forests, Bagging and Boosting}. 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 \textbf{convolutional neural networks} or \textbf{recurrent neural networks}, 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 \textbf{Principal Component Analysis} and \textbf{k-means Clustering}. \end{enumerate} \noindent @@ -171,7 +173,7 @@ We propose also an alternative to the above. This is a project on using machine 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 \href{{https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html}}{from week 42} and/or the textbook by \href{{https://www.springer.com/gp/book/9789401798150}}{Yadav et al}. -\textbf{Note}: 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. +\textbf{Note}: 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. \subsection*{The basic structure of your project} @@ -278,7 +280,7 @@ eigenvalues. Compare with the solution from numerical diagonalization with stand \paragraph{Part e).} 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. -\subsection*{Additonal (optional) exercise, adding 30 more points to final score} +\subsection*{Additonal (optional) exercise, adding 20 more points to final score} 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 diff --git a/doc/src/Projects/2022/Project3/Project3.do.txt b/doc/src/Projects/2022/Project3/Project3.do.txt index 03ef970eb..8572bfc4e 100644 --- a/doc/src/Projects/2022/Project3/Project3.do.txt +++ b/doc/src/Projects/2022/Project3/Project3.do.txt @@ -12,8 +12,12 @@ o "Kaggle":"https://www.kaggle.com/datasets" o The "University of California at Irvine (UCI) with its machine learning repository":"https://archive.ics.uci.edu/ml/index.php". o Or other sources. + +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. + + 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: -o Whether you end up with a regression or a classification problem, you should employ at least two of the methods we have discussed among _linear regression (including Ridge and Lasso)_, _Logistic Regression_, _Neural Networks_, _Convolution Neural Networks_, _Recurrent Neural Networks_, _Adversarial Neural Networks_, _Support Vector Machines_ and _Decision Trees, Random Forests, Bagging and Boosting_. 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 _convolutional neural networks_ or _recurrent neural networks_, 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 _Principal Component Analysis_ and _k-means Clustering_. +o Whether you end up with a regression or a classification problem, you should employ at least two of the methods we have discussed among _linear regression (including Ridge and Lasso)_, _Logistic Regression_, _Neural Networks_, _Convolution Neural Networks_, _Recurrent Neural Networks_, _Adversarial Neural Networks_, _Support Vector Machines_ and _Decision Trees, Random Forests, Bagging and Boosting_. 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 _convolutional neural networks_ or _recurrent neural networks_, 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 _Principal Component Analysis_ and _k-means Clustering_. For Boosting, feel also free to write your own codes. @@ -34,7 +38,7 @@ We propose also an alternative to the above. This is a project on using machine 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 "from week 42":"https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week42.html" and/or the textbook by "Yadav et al":"https://www.springer.com/gp/book/9789401798150". -_Note_: 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. +_Note_: 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. ===== The basic structure of your project ===== @@ -171,7 +175,7 @@ eigenvalues. Compare with the solution from numerical diagonalization with stand 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. -===== Additonal (optional) exercise, adding 30 more points to final score ===== +===== Additonal (optional) exercise, adding 20 more points to final score ===== 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