From 0a91cbc852ebdec1f6ebc6e1c6217be9e678e65e Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Mon, 13 Nov 2023 06:15:13 +0100 Subject: [PATCH] adding project updates --- doc/LectureNotes/Project2.ipynb | 34 ++--- doc/LectureNotes/Project3.ipynb | 118 +++++++++++------- .../_build/.doctrees/Project3.doctree | Bin 62569 -> 67625 bytes .../_build/.doctrees/environment.pickle | Bin 628907 -> 630112 bytes .../_build/.doctrees/project2.doctree | Bin 71272 -> 71276 bytes doc/LectureNotes/_build/html/Project3.html | 56 +++++++-- .../_build/html/_sources/Project3.ipynb | 118 +++++++++++------- .../_build/html/_sources/project2.ipynb | 34 ++--- doc/LectureNotes/_build/html/genindex.html | 2 +- doc/LectureNotes/_build/html/intro.html | 2 +- doc/LectureNotes/_build/html/objects.inv | Bin 1345 -> 1345 bytes doc/LectureNotes/_build/html/project2.html | 17 ++- doc/LectureNotes/_build/html/search.html | 2 +- doc/LectureNotes/_build/html/searchindex.js | 2 +- .../_build/jupyter_execute/Project3.ipynb | 118 +++++++++++------- .../_build/jupyter_execute/project2.ipynb | 34 ++--- 16 files changed, 332 insertions(+), 205 deletions(-) diff --git a/doc/LectureNotes/Project2.ipynb b/doc/LectureNotes/Project2.ipynb index 88c450dfd..882658ddc 100644 --- a/doc/LectureNotes/Project2.ipynb +++ b/doc/LectureNotes/Project2.ipynb @@ -2,34 +2,34 @@ "cells": [ { "cell_type": "markdown", - "id": "6afd3fdb", + "id": "515c9474", "metadata": { "editable": true }, "source": [ "\n", - "" + "" ] }, { "cell_type": "markdown", - "id": "21a10baa", + "id": "890dcb04", "metadata": { "editable": true }, "source": [ - "# Project 2 on Machine Learning, deadline November 13 (Midnight)\n", + "# Project 2 on Machine Learning, deadline November 17 (Midnight)\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: **Oct 9, 2023**\n", + "Date: **Nov 13, 2023**\n", "\n", "Copyright 1999-2023, [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": "4f67ce6e", + "id": "8cbf512f", "metadata": { "editable": true }, @@ -75,7 +75,7 @@ }, { "cell_type": "markdown", - "id": "67c6916e", + "id": "696379a0", "metadata": { "editable": true }, @@ -127,7 +127,7 @@ }, { "cell_type": "markdown", - "id": "fff54502", + "id": "81d48303", "metadata": { "editable": true }, @@ -166,7 +166,7 @@ }, { "cell_type": "markdown", - "id": "6e0f9012", + "id": "db6aebb0", "metadata": { "editable": true }, @@ -178,7 +178,7 @@ }, { "cell_type": "markdown", - "id": "075046bf", + "id": "18314d90", "metadata": { "editable": true }, @@ -204,7 +204,7 @@ }, { "cell_type": "markdown", - "id": "080dadf2", + "id": "dc3021aa", "metadata": { "editable": true }, @@ -216,7 +216,7 @@ }, { "cell_type": "markdown", - "id": "e3418dd4", + "id": "3c3c5b42", "metadata": { "editable": true }, @@ -236,7 +236,7 @@ }, { "cell_type": "markdown", - "id": "828ad79f", + "id": "1a493d07", "metadata": { "editable": true }, @@ -258,7 +258,7 @@ }, { "cell_type": "markdown", - "id": "07059668", + "id": "649a5380", "metadata": { "editable": true }, @@ -274,7 +274,7 @@ }, { "cell_type": "markdown", - "id": "e88200db", + "id": "804df082", "metadata": { "editable": true }, @@ -290,7 +290,7 @@ }, { "cell_type": "markdown", - "id": "66733be9", + "id": "2661bc83", "metadata": { "editable": true }, @@ -321,7 +321,7 @@ }, { "cell_type": "markdown", - "id": "5c49a400", + "id": "e651a157", "metadata": { "editable": true }, diff --git a/doc/LectureNotes/Project3.ipynb b/doc/LectureNotes/Project3.ipynb index d45c511e8..d4e676b9c 100644 --- a/doc/LectureNotes/Project3.ipynb +++ b/doc/LectureNotes/Project3.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "5831c36a", + "id": "32bbc99a", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "2d774c8b", + "id": "21397747", "metadata": { "editable": true }, @@ -22,14 +22,14 @@ "# Project 3 on Machine Learning, deadline December 18 (midnight), 2023\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 12, 2023**\n", + "Date: **Nov 13, 2023**\n", "\n", "Copyright 1999-2023, [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": "5cbde03b", + "id": "a13debcc", "metadata": { "editable": true }, @@ -39,7 +39,7 @@ }, { "cell_type": "markdown", - "id": "5170403b", + "id": "9d1f1220", "metadata": { "editable": true }, @@ -54,11 +54,15 @@ "3. Or other sources.\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**, 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. \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**, and **Decision Trees, Random Forests, Bagging and Boosting**.\n", + "\n", + "Feel also free to use support vector machines, $k$-means and principal components analysis, although the latter have not been covered during the lectures. This material can be found in the lecture notes.\n", + "\n", + "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. \n", "\n", "For Boosting, feel also free to write your own codes.\n", "\n", - "1. For project 3, you should feel free to use your own codes from projects 1 and 2, eventually write your own for Decision trees/random forests/bagging/boosting' or use the available functionality of **Scikit-Learn**, **Tensorflow**, etc. \n", + "1. For project 3, you should feel free to use your own codes from projects 1 and 2, eventually write your own for Decision trees/random forests/bagging/boosting' or use the available functionality of **Scikit-Learn**, **Tensorflow**, PyTorch etc. \n", "\n", "2. The estimates you used and tested in projects 1 and 2 should also be included, that is the $R2$-score, **MSE**, confusion matrix, accuracy score, information gain, ROC and Cumulative gains curves and other, cross-validation and/or bootstrap if these are relevant.\n", "\n", @@ -72,12 +76,12 @@ "\n", "We propose also an alternative to the above. This is a project on using machine learning methods (neural networks mainly) to the solution of ordinary differential equations and partial differential equations, with a final twist on how to diagonalize a symmetric matrix with neural networks.\n", "\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 43](https://compphysics.github.io/MachineLearning/doc/pub/week43/html/week42.html) and/or the textbook by [Yadav et al](https://www.springer.com/gp/book/9789401798150)." + "This is a field with large scientific interest, 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 43](https://compphysics.github.io/MachineLearning/doc/pub/week43/html/week42.html) and/or the textbook by [Yadav et al](https://www.springer.com/gp/book/9789401798150)." ] }, { "cell_type": "markdown", - "id": "e9fc8e8e", + "id": "6416060c", "metadata": { "editable": true }, @@ -89,7 +93,7 @@ }, { "cell_type": "markdown", - "id": "ce8b52a3", + "id": "18827262", "metadata": { "editable": true }, @@ -101,7 +105,7 @@ }, { "cell_type": "markdown", - "id": "eb943f7a", + "id": "bcee53f4", "metadata": { "editable": true }, @@ -113,7 +117,7 @@ }, { "cell_type": "markdown", - "id": "17447dcc", + "id": "83ec8275", "metadata": { "editable": true }, @@ -125,7 +129,7 @@ }, { "cell_type": "markdown", - "id": "d9bea856", + "id": "2be62c8e", "metadata": { "editable": true }, @@ -137,7 +141,7 @@ }, { "cell_type": "markdown", - "id": "5b74282b", + "id": "385e0b16", "metadata": { "editable": true }, @@ -149,7 +153,7 @@ }, { "cell_type": "markdown", - "id": "28f082d7", + "id": "fbf49165", "metadata": { "editable": true }, @@ -162,14 +166,27 @@ "equation in one dimension using a standard explicit scheme and neural\n", "networks to solve the same equations.\n", "\n", - "For the explicit scheme, you can study for example chapter 10 of the lecture notes in [Computational Physics](https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf) or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by [Kristine Baluka Hein and included in the lectures of week 43](https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week43.html) at this course are highly recommended.\n", + "For the explicit scheme, you can study for example chapter 10 of the lecture notes in [Computational Physics, FYS3150/4150](https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf) or alternative sources from courses like [MAT-MEK4270](https://www.uio.no/studier/emner/matnat/math/MAT-MEK4270/index.html). For the solution of ordinary and partial differential equations using neural networks, the lectures by [included in the lectures of week 43](https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week43.html) at this course are highly recommended.\n", "\n", - "For the machine learning part you can use your own code from project 2 or the functionality of for example **Tensorflow/Keras**.." + "For the machine learning part you can use your own code from project 2 or the functionality of for example **Tensorflow/Keras**, **PyTorch** or other libraries such [Physics informed machine learning](https://maziarraissi.github.io/PINNs/)." ] }, { "cell_type": "markdown", - "id": "5e855e7f", + "id": "c97df4f9", + "metadata": { + "editable": true + }, + "source": [ + "### Alternative differential equations\n", + "\n", + "Note that you can replace the one-dimensional diffusion equation discussed below with other sets of either ordinary differential equations or partial differential equations.\n", + "Please discuss such a change with us at the lab." + ] + }, + { + "cell_type": "markdown", + "id": "ecde0a0e", "metadata": { "editable": true }, @@ -183,7 +200,7 @@ }, { "cell_type": "markdown", - "id": "bc4be75f", + "id": "56429d7e", "metadata": { "editable": true }, @@ -195,7 +212,7 @@ }, { "cell_type": "markdown", - "id": "caa1eca4", + "id": "c2e49662", "metadata": { "editable": true }, @@ -205,7 +222,7 @@ }, { "cell_type": "markdown", - "id": "98a2bd5b", + "id": "fd661d63", "metadata": { "editable": true }, @@ -217,7 +234,7 @@ }, { "cell_type": "markdown", - "id": "980b949d", + "id": "a1d77183", "metadata": { "editable": true }, @@ -227,7 +244,7 @@ }, { "cell_type": "markdown", - "id": "7401e9ec", + "id": "73d187ef", "metadata": { "editable": true }, @@ -239,7 +256,7 @@ }, { "cell_type": "markdown", - "id": "e953662e", + "id": "b9b49da0", "metadata": { "editable": true }, @@ -250,7 +267,7 @@ }, { "cell_type": "markdown", - "id": "52122eb7", + "id": "aee685e3", "metadata": { "editable": true }, @@ -262,7 +279,7 @@ }, { "cell_type": "markdown", - "id": "ab71a383", + "id": "96ec18ae", "metadata": { "editable": true }, @@ -272,7 +289,7 @@ }, { "cell_type": "markdown", - "id": "4f886681", + "id": "57c542b7", "metadata": { "editable": true }, @@ -284,7 +301,7 @@ }, { "cell_type": "markdown", - "id": "83f8d12e", + "id": "b6e8e863", "metadata": { "editable": true }, @@ -297,7 +314,7 @@ }, { "cell_type": "markdown", - "id": "4bcf494f", + "id": "76ae9476", "metadata": { "editable": true }, @@ -309,7 +326,7 @@ }, { "cell_type": "markdown", - "id": "7f95982b", + "id": "95e239a3", "metadata": { "editable": true }, @@ -319,7 +336,7 @@ }, { "cell_type": "markdown", - "id": "1ee81ac0", + "id": "63fb1417", "metadata": { "editable": true }, @@ -331,7 +348,7 @@ }, { "cell_type": "markdown", - "id": "bae7898a", + "id": "e1c77709", "metadata": { "editable": true }, @@ -341,7 +358,7 @@ }, { "cell_type": "markdown", - "id": "5de23ea9", + "id": "2631e4bd", "metadata": { "editable": true }, @@ -353,7 +370,7 @@ }, { "cell_type": "markdown", - "id": "4d5d56e3", + "id": "0fa54ca3", "metadata": { "editable": true }, @@ -364,7 +381,7 @@ }, { "cell_type": "markdown", - "id": "e52523ef", + "id": "7579152d", "metadata": { "editable": true }, @@ -380,7 +397,7 @@ }, { "cell_type": "markdown", - "id": "678607d0", + "id": "e8ceb962", "metadata": { "editable": true }, @@ -397,21 +414,34 @@ }, { "cell_type": "markdown", - "id": "61d51d93", + "id": "fe5c3f2f", "metadata": { "editable": true }, "source": [ - "### Part d)\n", + "### Part d) Neural network complexity\n", "\n", - "Finally, present a critical assessment of the methods you have studied\n", - "and discuss the potential for the solving differential equations and\n", - "eigenvalue problems with machine learning methods." + "Here we study the stability of the results of the results as functions of the number of hidden nodes, layers and activation functions for the hidden layers.\n", + "Increase the number of hidden nodes and layers in order to see if this improves your results. Try also different activation functions for the hidden layers, such as the **tanh**, **ReLU**, and other activation functions. \n", + "Discuss your results." ] }, { "cell_type": "markdown", - "id": "d2163034", + "id": "3b2adf3a", + "metadata": { + "editable": true + }, + "source": [ + "### Part e)\n", + "\n", + "Finally, present a critical assessment of the methods you have studied\n", + "and discuss the potential for the solving differential equations with machine learning methods." + ] + }, + { + "cell_type": "markdown", + "id": "c2ed7243", "metadata": { "editable": true }, @@ -442,7 +472,7 @@ }, { "cell_type": "markdown", - "id": "e31290fd", + "id": "e9f450e7", "metadata": { "editable": true }, @@ -464,7 +494,7 @@ }, { "cell_type": "markdown", - "id": "99e78b73", + "id": "4ec24e55", "metadata": { "editable": true }, diff --git a/doc/LectureNotes/_build/.doctrees/Project3.doctree b/doc/LectureNotes/_build/.doctrees/Project3.doctree index 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  • - Project 2 on Machine Learning, deadline November 13 (Midnight) + Project 2 on Machine Learning, deadline November 17 (Midnight)
    • @@ -496,6 +496,11 @@ const thebe_selector_output = ".output, .cell_output" Solving partial differential equations with neural networks @@ -607,6 +617,11 @@ const thebe_selector_output = ".output, .cell_output" Solving partial differential equations with neural networks @@ -660,7 +680,7 @@ doconce format html Project3.do.txt -->

    Project 3 on Machine Learning, deadline December 18 (midnight), 2023¶

    Data Analysis and Machine Learning FYS-STK3155/FYS4155, Department of Physics, University of Oslo, Norway

    -

    Date: Nov 12, 2023

    +

    Date: Nov 13, 2023

    Copyright 1999-2023, Data Analysis and Machine Learning FYS-STK3155/FYS4155. Released under CC Attribution-NonCommercial 4.0 license

    @@ -675,11 +695,13 @@ doconce format html Project3.do.txt -->

    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:

      -

    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, 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.

      2. +

    2. 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, and Decision Trees, Random Forests, Bagging and Boosting.

    +

    Feel also free to use support vector machines, \(k\)-means and principal components analysis, although the latter have not been covered during the lectures. This material can be found in the lecture notes.

    +

    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.

    For Boosting, feel also free to write your own codes.

      -

    1. For project 3, you should feel free to use your own codes from projects 1 and 2, eventually write your own for Decision trees/random forests/bagging/boosting’ or use the available functionality of Scikit-Learn, Tensorflow, etc.

      2. +

    2. For project 3, you should feel free to use your own codes from projects 1 and 2, eventually write your own for Decision trees/random forests/bagging/boosting’ or use the available functionality of Scikit-Learn, Tensorflow, PyTorch etc.

    3. The estimates you used and tested in projects 1 and 2 should also be included, that is the \(R2\)-score, MSE, confusion matrix, accuracy score, information gain, ROC and Cumulative gains curves and other, cross-validation and/or bootstrap if these are relevant.

    4. Similarly, feel free to explore various activations functions in deep learning and various approachs to stochastic gradient descent approaches.

    5. If possible, you should link the data sets with exisiting research and analyses thereof. Scientific articles which have used Machine Learning algorithms to analyze the data are highly welcome. Perhaps you can improve previous analyses and even publish a new article?

      6. @@ -687,7 +709,7 @@ doconce format html Project3.do.txt -->

    All in all, the report should follow the same pattern as the two previous ones, with abstract, introduction, methods, code, results, conclusions etc..

    We propose also an alternative to the above. This is a project on using machine learning methods (neural networks mainly) to the solution of ordinary differential equations and partial differential equations, with a final twist on how to diagonalize a symmetric matrix with neural networks.

    -

    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 43 and/or the textbook by Yadav et al.

    +

    This is a field with large scientific interest, 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 43 and/or the textbook by Yadav et al.

    The basic structure of your project¶

    @@ -720,8 +742,13 @@ background in the solution of partial differential equations using finite difference schemes. We will study the solution of the diffusion equation in one dimension using a standard explicit scheme and neural networks to solve the same equations.

    -

    For the explicit scheme, you can study for example chapter 10 of the lecture notes in Computational Physics or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by Kristine Baluka Hein and included in the lectures of week 43 at this course are highly recommended.

    -

    For the machine learning part you can use your own code from project 2 or the functionality of for example Tensorflow/Keras..

    +

    For the explicit scheme, you can study for example chapter 10 of the lecture notes in Computational Physics, FYS3150/4150 or alternative sources from courses like MAT-MEK4270. For the solution of ordinary and partial differential equations using neural networks, the lectures by included in the lectures of week 43 at this course are highly recommended.

    +

    For the machine learning part you can use your own code from project 2 or the functionality of for example Tensorflow/Keras, PyTorch or other libraries such Physics informed machine learning.

    +
    +

    Alternative differential equations¶

    +

    Note that you can replace the one-dimensional diffusion equation discussed below with other sets of either ordinary differential equations or partial differential equations. +Please discuss such a change with us at the lab.

    +

    Part a), setting up the problem¶

    The physical problem can be that of the temperature gradient in a rod of length \(L=1\) at \(x=0\) and \(x=1\). @@ -788,11 +815,16 @@ part b). Discuss your results and compare them with the standard explicit scheme. Include also the analytical solution and compare with that.

    +
    +

    Part d) Neural network complexity¶

    +

    Here we study the stability of the results of the results as functions of the number of hidden nodes, layers and activation functions for the hidden layers. +Increase the number of hidden nodes and layers in order to see if this improves your results. Try also different activation functions for the hidden layers, such as the tanh, ReLU, and other activation functions. +Discuss your results.

    +
    -

    Part d)¶

    +

    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.

    +and discuss the potential for the solving differential equations with machine learning methods.

    diff --git a/doc/LectureNotes/_build/html/_sources/Project3.ipynb b/doc/LectureNotes/_build/html/_sources/Project3.ipynb index d45c511e8..d4e676b9c 100644 --- a/doc/LectureNotes/_build/html/_sources/Project3.ipynb +++ b/doc/LectureNotes/_build/html/_sources/Project3.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "5831c36a", + "id": "32bbc99a", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "2d774c8b", + "id": "21397747", "metadata": { "editable": true }, @@ -22,14 +22,14 @@ "# Project 3 on Machine Learning, deadline December 18 (midnight), 2023\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 12, 2023**\n", + "Date: **Nov 13, 2023**\n", "\n", "Copyright 1999-2023, [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": "5cbde03b", + "id": "a13debcc", "metadata": { "editable": true }, @@ -39,7 +39,7 @@ }, { "cell_type": "markdown", - "id": "5170403b", + "id": "9d1f1220", "metadata": { "editable": true }, @@ -54,11 +54,15 @@ "3. Or other sources.\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**, 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. \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**, and **Decision Trees, Random Forests, Bagging and Boosting**.\n", + "\n", + "Feel also free to use support vector machines, $k$-means and principal components analysis, although the latter have not been covered during the lectures. This material can be found in the lecture notes.\n", + "\n", + "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. \n", "\n", "For Boosting, feel also free to write your own codes.\n", "\n", - "1. For project 3, you should feel free to use your own codes from projects 1 and 2, eventually write your own for Decision trees/random forests/bagging/boosting' or use the available functionality of **Scikit-Learn**, **Tensorflow**, etc. \n", + "1. For project 3, you should feel free to use your own codes from projects 1 and 2, eventually write your own for Decision trees/random forests/bagging/boosting' or use the available functionality of **Scikit-Learn**, **Tensorflow**, PyTorch etc. \n", "\n", "2. The estimates you used and tested in projects 1 and 2 should also be included, that is the $R2$-score, **MSE**, confusion matrix, accuracy score, information gain, ROC and Cumulative gains curves and other, cross-validation and/or bootstrap if these are relevant.\n", "\n", @@ -72,12 +76,12 @@ "\n", "We propose also an alternative to the above. This is a project on using machine learning methods (neural networks mainly) to the solution of ordinary differential equations and partial differential equations, with a final twist on how to diagonalize a symmetric matrix with neural networks.\n", "\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 43](https://compphysics.github.io/MachineLearning/doc/pub/week43/html/week42.html) and/or the textbook by [Yadav et al](https://www.springer.com/gp/book/9789401798150)." + "This is a field with large scientific interest, 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 43](https://compphysics.github.io/MachineLearning/doc/pub/week43/html/week42.html) and/or the textbook by [Yadav et al](https://www.springer.com/gp/book/9789401798150)." ] }, { "cell_type": "markdown", - "id": "e9fc8e8e", + "id": "6416060c", "metadata": { "editable": true }, @@ -89,7 +93,7 @@ }, { "cell_type": "markdown", - "id": "ce8b52a3", + "id": "18827262", "metadata": { "editable": true }, @@ -101,7 +105,7 @@ }, { "cell_type": "markdown", - "id": "eb943f7a", + "id": "bcee53f4", "metadata": { "editable": true }, @@ -113,7 +117,7 @@ }, { "cell_type": "markdown", - "id": "17447dcc", + "id": "83ec8275", "metadata": { "editable": true }, @@ -125,7 +129,7 @@ }, { "cell_type": "markdown", - "id": "d9bea856", + "id": "2be62c8e", "metadata": { "editable": true }, @@ -137,7 +141,7 @@ }, { "cell_type": "markdown", - "id": "5b74282b", + "id": "385e0b16", "metadata": { "editable": true }, @@ -149,7 +153,7 @@ }, { "cell_type": "markdown", - "id": "28f082d7", + "id": "fbf49165", "metadata": { "editable": true }, @@ -162,14 +166,27 @@ "equation in one dimension using a standard explicit scheme and neural\n", "networks to solve the same equations.\n", "\n", - "For the explicit scheme, you can study for example chapter 10 of the lecture notes in [Computational Physics](https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf) or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by [Kristine Baluka Hein and included in the lectures of week 43](https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week43.html) at this course are highly recommended.\n", + "For the explicit scheme, you can study for example chapter 10 of the lecture notes in [Computational Physics, FYS3150/4150](https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf) or alternative sources from courses like [MAT-MEK4270](https://www.uio.no/studier/emner/matnat/math/MAT-MEK4270/index.html). For the solution of ordinary and partial differential equations using neural networks, the lectures by [included in the lectures of week 43](https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week43.html) at this course are highly recommended.\n", "\n", - "For the machine learning part you can use your own code from project 2 or the functionality of for example **Tensorflow/Keras**.." + "For the machine learning part you can use your own code from project 2 or the functionality of for example **Tensorflow/Keras**, **PyTorch** or other libraries such [Physics informed machine learning](https://maziarraissi.github.io/PINNs/)." ] }, { "cell_type": "markdown", - "id": "5e855e7f", + "id": "c97df4f9", + "metadata": { + "editable": true + }, + "source": [ + "### Alternative differential equations\n", + "\n", + "Note that you can replace the one-dimensional diffusion equation discussed below with other sets of either ordinary differential equations or partial differential equations.\n", + "Please discuss such a change with us at the lab." + ] + }, + { + "cell_type": "markdown", + "id": "ecde0a0e", "metadata": { "editable": true }, @@ -183,7 +200,7 @@ }, { "cell_type": "markdown", - "id": "bc4be75f", + "id": "56429d7e", "metadata": { "editable": true }, @@ -195,7 +212,7 @@ }, { "cell_type": "markdown", - "id": "caa1eca4", + "id": "c2e49662", "metadata": { "editable": true }, @@ -205,7 +222,7 @@ }, { "cell_type": "markdown", - "id": "98a2bd5b", + "id": "fd661d63", "metadata": { "editable": true }, @@ -217,7 +234,7 @@ }, { "cell_type": "markdown", - "id": "980b949d", + "id": "a1d77183", "metadata": { "editable": true }, @@ -227,7 +244,7 @@ }, { "cell_type": "markdown", - "id": "7401e9ec", + "id": "73d187ef", "metadata": { "editable": true }, @@ -239,7 +256,7 @@ }, { "cell_type": "markdown", - "id": "e953662e", + "id": "b9b49da0", "metadata": { "editable": true }, @@ -250,7 +267,7 @@ }, { "cell_type": "markdown", - "id": "52122eb7", + "id": "aee685e3", "metadata": { "editable": true }, @@ -262,7 +279,7 @@ }, { "cell_type": "markdown", - "id": "ab71a383", + "id": "96ec18ae", "metadata": { "editable": true }, @@ -272,7 +289,7 @@ }, { "cell_type": "markdown", - "id": "4f886681", + "id": "57c542b7", "metadata": { "editable": true }, @@ -284,7 +301,7 @@ }, { "cell_type": "markdown", - "id": "83f8d12e", + "id": "b6e8e863", "metadata": { "editable": true }, @@ -297,7 +314,7 @@ }, { "cell_type": "markdown", - "id": "4bcf494f", + "id": "76ae9476", "metadata": { "editable": true }, @@ -309,7 +326,7 @@ }, { "cell_type": "markdown", - "id": "7f95982b", + "id": "95e239a3", "metadata": { "editable": true }, @@ -319,7 +336,7 @@ }, { "cell_type": "markdown", - "id": "1ee81ac0", + "id": "63fb1417", "metadata": { "editable": true }, @@ -331,7 +348,7 @@ }, { "cell_type": "markdown", - "id": "bae7898a", + "id": "e1c77709", "metadata": { "editable": true }, @@ -341,7 +358,7 @@ }, { "cell_type": "markdown", - "id": "5de23ea9", + "id": "2631e4bd", "metadata": { "editable": true }, @@ -353,7 +370,7 @@ }, { "cell_type": "markdown", - "id": "4d5d56e3", + "id": "0fa54ca3", "metadata": { "editable": true }, @@ -364,7 +381,7 @@ }, { "cell_type": "markdown", - "id": "e52523ef", + "id": "7579152d", "metadata": { "editable": true }, @@ -380,7 +397,7 @@ }, { "cell_type": "markdown", - "id": "678607d0", + "id": "e8ceb962", "metadata": { "editable": true }, @@ -397,21 +414,34 @@ }, { "cell_type": "markdown", - "id": "61d51d93", + "id": "fe5c3f2f", "metadata": { "editable": true }, "source": [ - "### Part d)\n", + "### Part d) Neural network complexity\n", "\n", - "Finally, present a critical assessment of the methods you have studied\n", - "and discuss the potential for the solving differential equations and\n", - "eigenvalue problems with machine learning methods." + "Here we study the stability of the results of the results as functions of the number of hidden nodes, layers and activation functions for the hidden layers.\n", + "Increase the number of hidden nodes and layers in order to see if this improves your results. Try also different activation functions for the hidden layers, such as the **tanh**, **ReLU**, and other activation functions. \n", + "Discuss your results." ] }, { "cell_type": "markdown", - "id": "d2163034", + "id": "3b2adf3a", + "metadata": { + "editable": true + }, + "source": [ + "### Part e)\n", + "\n", + "Finally, present a critical assessment of the methods you have studied\n", + "and discuss the potential for the solving differential equations with machine learning methods." + ] + }, + { + "cell_type": "markdown", + "id": "c2ed7243", "metadata": { "editable": true }, @@ -442,7 +472,7 @@ }, { "cell_type": "markdown", - "id": "e31290fd", + "id": "e9f450e7", "metadata": { "editable": true }, @@ -464,7 +494,7 @@ }, { "cell_type": "markdown", - "id": "99e78b73", + "id": "4ec24e55", "metadata": { "editable": true }, diff --git a/doc/LectureNotes/_build/html/_sources/project2.ipynb b/doc/LectureNotes/_build/html/_sources/project2.ipynb index 88c450dfd..882658ddc 100644 --- a/doc/LectureNotes/_build/html/_sources/project2.ipynb +++ b/doc/LectureNotes/_build/html/_sources/project2.ipynb @@ -2,34 +2,34 @@ "cells": [ { "cell_type": "markdown", - "id": "6afd3fdb", + "id": "515c9474", "metadata": { "editable": true }, "source": [ "\n", - "" + "" ] }, { "cell_type": "markdown", - "id": "21a10baa", + "id": "890dcb04", "metadata": { "editable": true }, "source": [ - "# Project 2 on Machine Learning, deadline November 13 (Midnight)\n", + "# Project 2 on Machine Learning, deadline November 17 (Midnight)\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: **Oct 9, 2023**\n", + "Date: **Nov 13, 2023**\n", "\n", "Copyright 1999-2023, [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": "4f67ce6e", + "id": "8cbf512f", "metadata": { "editable": true }, @@ -75,7 +75,7 @@ }, { "cell_type": "markdown", - "id": "67c6916e", + "id": "696379a0", "metadata": { "editable": true }, @@ -127,7 +127,7 @@ }, { "cell_type": "markdown", - "id": "fff54502", + "id": "81d48303", "metadata": { "editable": true }, @@ -166,7 +166,7 @@ }, { "cell_type": "markdown", - "id": "6e0f9012", + "id": "db6aebb0", "metadata": { "editable": true }, @@ -178,7 +178,7 @@ }, { "cell_type": "markdown", - "id": "075046bf", + "id": "18314d90", "metadata": { "editable": true }, @@ -204,7 +204,7 @@ }, { "cell_type": "markdown", - "id": "080dadf2", + "id": "dc3021aa", "metadata": { "editable": true }, @@ -216,7 +216,7 @@ }, { "cell_type": "markdown", - "id": "e3418dd4", + "id": "3c3c5b42", "metadata": { "editable": true }, @@ -236,7 +236,7 @@ }, { "cell_type": "markdown", - "id": "828ad79f", + "id": "1a493d07", "metadata": { "editable": true }, @@ -258,7 +258,7 @@ }, { "cell_type": "markdown", - "id": "07059668", + "id": "649a5380", "metadata": { "editable": true }, @@ -274,7 +274,7 @@ }, { "cell_type": "markdown", - "id": "e88200db", + "id": "804df082", "metadata": { "editable": true }, @@ -290,7 +290,7 @@ }, { "cell_type": "markdown", - "id": "66733be9", + "id": "2661bc83", "metadata": { "editable": true }, @@ -321,7 +321,7 @@ }, { "cell_type": "markdown", - "id": "5c49a400", + "id": "e651a157", "metadata": { "editable": true }, diff --git a/doc/LectureNotes/_build/html/genindex.html b/doc/LectureNotes/_build/html/genindex.html index 7bf64532e..5032cbc05 100644 --- a/doc/LectureNotes/_build/html/genindex.html +++ b/doc/LectureNotes/_build/html/genindex.html @@ -368,7 +368,7 @@ const thebe_selector_output = ".output, .cell_output"
  • - Project 2 on Machine Learning, deadline November 13 (Midnight) + Project 2 on Machine Learning, deadline November 17 (Midnight)
    • diff --git a/doc/LectureNotes/_build/html/intro.html b/doc/LectureNotes/_build/html/intro.html index ec26b1763..b849ab691 100644 --- a/doc/LectureNotes/_build/html/intro.html +++ b/doc/LectureNotes/_build/html/intro.html @@ -369,7 +369,7 @@ const thebe_selector_output = ".output, .cell_output"
  • - Project 2 on Machine Learning, deadline November 13 (Midnight) + Project 2 on Machine Learning, deadline November 17 (Midnight)
    • diff --git a/doc/LectureNotes/_build/html/objects.inv b/doc/LectureNotes/_build/html/objects.inv index 300fd36d186cd69f893b65b90492223c208878b4..2edd0fda77a9d3e6881a3c68fff6e0277e433668 100644 GIT binary patch delta 20 ccmX@eb&zXAAv06tq|L?5rx;mOMMBO408M`e`~Uy| delta 20 ccmX@eb&zXAAv4p=iJOa=PcgFaaD - Project 2 on Machine Learning, deadline November 13 (Midnight) — Applied Data Analysis and Machine Learning + Project 2 on Machine Learning, deadline November 17 (Midnight) — Applied Data Analysis and Machine Learning @@ -352,6 +352,11 @@ const thebe_selector_output = ".output, .cell_output" Week 45, Recurrent Neural Networks +
  • + + Week 46: Decision Trees, Ensemble methods and Random Forests + +

    • @@ -366,7 +371,7 @@ const thebe_selector_output = ".output, .cell_output"

  • - Project 2 on Machine Learning, deadline November 13 (Midnight) + Project 2 on Machine Learning, deadline November 17 (Midnight)
    • @@ -502,7 +507,7 @@ const thebe_selector_output = ".output, .cell_output"
    -

    Project 2 on Machine Learning, deadline November 13 (Midnight)

    +

    Project 2 on Machine Learning, deadline November 17 (Midnight)

    @@ -575,10 +580,10 @@ const thebe_selector_output = ".output, .cell_output" -
    -

    Project 2 on Machine Learning, deadline November 13 (Midnight)¶

    +
    +

    Project 2 on Machine Learning, deadline November 17 (Midnight)¶

    Data Analysis and Machine Learning FYS-STK3155/FYS4155, Department of Physics, University of Oslo, Norway

    -

    Date: Oct 9, 2023

    +

    Date: Nov 13, 2023

    Copyright 1999-2023, Data Analysis and Machine Learning FYS-STK3155/FYS4155. Released under CC Attribution-NonCommercial 4.0 license

    Classification and Regression, from linear and logistic regression to neural networks¶

    diff --git a/doc/LectureNotes/_build/html/search.html b/doc/LectureNotes/_build/html/search.html index 775659784..4e6e035a3 100644 --- a/doc/LectureNotes/_build/html/search.html +++ b/doc/LectureNotes/_build/html/search.html @@ -374,7 +374,7 @@ const thebe_selector_output = ".output, .cell_output"
  • - Project 2 on Machine Learning, deadline November 13 (Midnight) + Project 2 on Machine Learning, deadline November 17 (Midnight)
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3 on Machine Learning, deadline December 18 (midnight), 2023","3. Linear Regression","14. Building a Feed Forward Neural Network","15. Solving Differential Equations with Deep Learning","16. Convolutional Neural Networks","17. Recurrent neural networks: Overarching view","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","12. Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Exercises week 39","Exercises week 41","Exercises week 42","Exercises weeks 43 and 44","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 9 (midnight), 2023","Project 2 on Machine Learning, deadline November 13 (Midnight)","Teaching schedule with links to material","1. Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Statistical interpretation of Linear Regression and Resampling techniques","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and Gradient Methods","Week 40: Gradient descent methods (continued) and start Neural networks","Week 41 Neural networks and constructing a neural network code","Week 42 Constructing a Neural Network code with introduction to Tensor flow","Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations","Week 44, Convolutional Neural Networks (CNN)","Week 45, Recurrent Neural Networks","Week 46: Decision Trees, Ensemble methods and Random Forests"],titleterms:{"1":[1,16,17,18,19,27,33,34],"12":40,"13":28,"14":36,"18":0,"19":41,"2":[1,16,17,18,19,28,33,34,43],"2023":[0,27,31],"21":37,"23":37,"26":42,"3":[0,1,16,17,33,34],"31":34,"34":[16,33],"35":[17,34],"36":[18,35],"37":[19,36],"38":[20,37],"39":[21,22,38],"3d":43,"4":[1,34],"40":[22,39],"41":[22,40],"42":[23,41],"43":[24,42],"44":[24,42,43],"45":44,"46":45,"5":1,"7":35,"9":[27,44],"case":[9,11,30,34,35,37,38],"class":37,"do":[2,33,35,36,38,39,40,41,42,45],"final":[13,22,28,34,35,38,39,40,42,43,45],"function":[1,2,7,8,9,11,12,13,14,22,24,27,28,30,33,34,35,36,37,38,39,40,41,42,43,45],"import":[6,22,26,33,34,35,43,45],"long":44,"new":[5,35,36,44],"short":44,"try":44,A:[1,2,5,9,10,22,33,35,36,37,41,42,43,44,45],AND:[24,39,40,41,42],And:[22,33,34,35,37,38,39],But:[22,38,39],In:31,Is:[41,42],Ising:7,No:43,OR:[39,40,41,42],The:[0,1,2,3,4,6,7,8,9,10,12,13,18,24,25,33,34,35,36,37,38,39,40,41,42,43,44,45],To:[33,34],With:[5,35,44],about:[33,34],abov:[24,35,42,43],activ:[2,13,24,28,35,39,40,41,42,43,44],ad:[1,7,18,27,33,34,39,40],adaboost:[11,45],adagrad:[14,22,38,39],adam:[14,22,38,39],adapt:[11,22,38,39,45],add:43,addit:[43,44,45],adjust:[2,40,41,42],advanc:22,adversari:5,again:[4,10,37,43,44],ai:33,aim:[9,10,18,19,20,21,22,23,24,33,45],aka:[33,34],al:22,algebra:[26,33],algorithm:[10,11,12,13,22,28,34,38,39,40,41,45],algortithm:[14,37,38],all:[9,44],an:[1,5,11,33,44,45],analys:[6,34],analysi:[1,6,7,12,25,27,28,30,33,34,35,36],analyt:[1,17,18,22,42],analyz:0,ani:[14,37,38],anoth:[10,35,36],appli:25,approach:[1,9,15,33,36,38,39,45],approxim:[13,40],architectur:[2,40,41,42],argument:[38,39],arithmet:43,arrai:[26,33],artifici:[39,40],assist:31,assumpt:[35,36],august:34,autocorrel:30,autograd:[3,14,22,38,39,42],automat:[14,22,38,39,42],avoid:[38,39],b:[0,18,27,28,38,39],back:[2,12,13,40,41,42],background:[25,27,28,36],backpropag:[43,44],backward:44,bag:[11,45],base:[14,22,36,38,39],basic:[0,1,6,8,10,11,12,26,34,35,36,37,44,45],batch:[2,38,39,41,42],bay:[6,35,36],befor:12,beta:[35,36],better:[9,39,40],bia:[7,27,36],bias:[40,41,42],binari:[2,40,41,45],bind:33,bird:[11,45],boldsymbol:[34,35,36],boost:[11,45],bootstrap:[7,11,36,45],boston:[1,34],breast:[2,41,42],brief:[33,36,37,38,43],bring:[13,40],build:[2,4,10,24,41,42,43,45],c:[0,27,28,33],calcul:34,can:[22,33,36,38,39,44],cancer:[2,8,10,12,37,41,42,44,45],cart:[10,45],cell:44,center:34,central:[14,25,30,36,37,38],chain:[13,40],challeng:37,chang:[11,45],channel:33,chi:[1,33],choic:[41,42],choos:[2,40,41,42],cifar01:[4,43],classic:12,classif:[2,10,11,24,28,37,40,41,42,44,45],classifi:[9,37,45],clip:[2,41,42],cluster:15,cnn:[4,43],code:[1,2,3,6,10,12,13,14,15,22,24,28,33,34,35,36,37,38,39,40,41,42,43,45],coeffici:43,coin:45,collect:[2,4,40,41,42,43],combin:44,come:37,commun:33,compact:37,compar:[3,11,42,45],comparison:35,compet:[22,38,39],compil:43,complet:34,complex:[1,7,27,34,45],complic:[7,38,39],compon:12,comput:[10,38,39,45],computation:36,computerlab:33,con:[10,45],concept:30,condit:[35,36,37,38],confid:36,conjug:[14,38],connect:43,construct:[40,41,42],content:43,continu:39,contn:33,convex:[9,14,37,38],convolut:[4,13,39,40,43],convolution2dlay:43,correctli:[35,36],correl:[12,34,37,43,44],correspond:[37,38],cost:[2,11,24,34,35,36,37,38,40,41,42,43,45],cours:[25,32,33],covari:[6,12,30,34],cover:33,critic:28,cross:[7,27,36,37],ct:43,cumul:44,curv:44,cython:33,d:[0,27,28],data:[0,1,2,4,7,8,10,12,16,17,24,25,27,30,33,34,35,37,40,41,42,43,44,45],dataset:[2,4,40,41,42,43],deadlin:[0,27,28,33],decai:[3,38,39,42],decemb:0,decis:[10,11,45],decomposit:[6,12,18,26,34],deep:[2,3,37,41,42],defin:[0,2,33,40,41,42],definit:40,degre:[1,34],deliveri:[0,27,28],delta:36,demonstr:43,dens:[1,33,43],deriv:[6,13,34,35,36,37,38,40,41,42],descent:[3,11,14,22,28,37,38,39,42],descript:27,design:34,detail:[4,33,42,43],develop:[2,40,41,42],diagon:12,differ:[9,28,38,39,43],different:22,differenti:[0,3,14,38,39,42],diffus:[3,42],dimension:[3,4,9,24,27,34,42,43],directli:[38,39],disadvantag:[10,45],discret:30,discuss:[37,44],distribut:[6,30,35,36],distrubut:[35,36],doe:[34,35,39,40],domain:[30,43],don:43,dot:[38,39],down:[2,41,42,45],dropout:[2,41,42],e:[0,27,28],each:37,economi:34,effect:44,effici:43,electron:[0,27,28],element:[1,30,33,38,39],elimin:26,energi:33,ensembl:[11,45],entropi:[10,37],environ:[1,16,33],equat:[0,1,3,13,33,34,35,37,38,40,42],error:[1,11,33,34,36,45],essenti:33,estim:[35,36],et:22,etc:[33,43],euler:[3,42],evalu:[2,28,40,41,42,43],exampl:[1,2,3,4,5,7,8,9,10,11,22,33,34,35,36,37,38,39,40,41,42,43,44,45],exercis:[1,7,16,17,18,19,20,21,22,23,24,33,34,36,42],expect:[19,30,35,36],expens:36,experi:30,explod:44,explor:[1,16,17,33],exponenti:[3,42],express:[18,19,33,34,37,38],extend:[37,38],extrapol:[5,44],extrem:[11,33,45],ey:[11,45],f:[27,28],f_1:44,factor:45,fall:31,famili:[2,33,34,41,42],famou:26,fantast:34,featur:[10,26,34,45],feed:[2,13,39,40,41,42],file:43,find:[36,38,39,43],fine:[2,41,42],first:[5,13,28,33,34,35,37,38,40,42,45],fit:[1,11,33,35,45],fix:34,flatt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3 on Machine Learning, deadline December 18 (midnight), 2023","3. Linear Regression","14. Building a Feed Forward Neural Network","15. Solving Differential Equations with Deep Learning","16. Convolutional Neural Networks","17. Recurrent neural networks: Overarching view","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","12. Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Exercises week 39","Exercises week 41","Exercises week 42","Exercises weeks 43 and 44","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 9 (midnight), 2023","Project 2 on Machine Learning, deadline November 17 (Midnight)","Teaching schedule with links to material","1. Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Statistical interpretation of Linear Regression and Resampling techniques","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and Gradient Methods","Week 40: Gradient descent methods (continued) and start Neural networks","Week 41 Neural networks and constructing a neural network code","Week 42 Constructing a Neural Network code with introduction to Tensor flow","Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations","Week 44, Convolutional Neural Networks (CNN)","Week 45, Recurrent Neural Networks","Week 46: Decision Trees, Ensemble methods and Random Forests"],titleterms:{"1":[1,16,17,18,19,27,33,34],"12":40,"13":[],"14":36,"17":28,"18":0,"19":41,"2":[1,16,17,18,19,28,33,34,43],"2023":[0,27,31],"21":37,"23":37,"26":42,"3":[0,1,16,17,33,34],"31":34,"34":[16,33],"35":[17,34],"36":[18,35],"37":[19,36],"38":[20,37],"39":[21,22,38],"3d":43,"4":[1,34],"40":[22,39],"41":[22,40],"42":[23,41],"43":[24,42],"44":[24,42,43],"45":44,"46":45,"5":1,"7":35,"9":[27,44],"case":[9,11,30,34,35,37,38],"class":37,"do":[2,33,35,36,38,39,40,41,42,45],"final":[13,22,28,34,35,38,39,40,42,43,45],"function":[1,2,7,8,9,11,12,13,14,22,24,27,28,30,33,34,35,36,37,38,39,40,41,42,43,45],"import":[6,22,26,33,34,35,43,45],"long":44,"new":[5,35,36,44],"short":44,"try":44,A:[1,2,5,9,10,22,33,35,36,37,41,42,43,44,45],AND:[24,39,40,41,42],And:[22,33,34,35,37,38,39],But:[22,38,39],In:31,Is:[41,42],Ising:7,No:43,OR:[39,40,41,42],The:[0,1,2,3,4,6,7,8,9,10,12,13,18,24,25,33,34,35,36,37,38,39,40,41,42,43,44,45],To:[33,34],With:[5,35,44],about:[33,34],abov:[24,35,42,43],activ:[2,13,24,28,35,39,40,41,42,43,44],ad:[1,7,18,27,33,34,39,40],adaboost:[11,45],adagrad:[14,22,38,39],adam:[14,22,38,39],adapt:[11,22,38,39,45],add:43,addit:[43,44,45],adjust:[2,40,41,42],advanc:22,adversari:5,again:[4,10,37,43,44],ai:33,aim:[9,10,18,19,20,21,22,23,24,33,45],aka:[33,34],al:22,algebra:[26,33],algorithm:[10,11,12,13,22,28,34,38,39,40,41,45],algortithm:[14,37,38],all:[9,44],altern:0,an:[1,5,11,33,44,45],analys:[6,34],analysi:[1,6,7,12,25,27,28,30,33,34,35,36],analyt:[1,17,18,22,42],analyz:0,ani:[14,37,38],anoth:[10,35,36],appli:25,approach:[1,9,15,33,36,38,39,45],approxim:[13,40],architectur:[2,40,41,42],argument:[38,39],arithmet:43,arrai:[26,33],artifici:[39,40],assist:31,assumpt:[35,36],august:34,autocorrel:30,autograd:[3,14,22,38,39,42],automat:[14,22,38,39,42],avoid:[38,39],b:[0,18,27,28,38,39],back:[2,12,13,40,41,42],background:[25,27,28,36],backpropag:[43,44],backward:44,bag:[11,45],base:[14,22,36,38,39],basic:[0,1,6,8,10,11,12,26,34,35,36,37,44,45],batch:[2,38,39,41,42],bay:[6,35,36],befor:12,beta:[35,36],better:[9,39,40],bia:[7,27,36],bias:[40,41,42],binari:[2,40,41,45],bind:33,bird:[11,45],boldsymbol:[34,35,36],boost:[11,45],bootstrap:[7,11,36,45],boston:[1,34],breast:[2,41,42],brief:[33,36,37,38,43],bring:[13,40],build:[2,4,10,24,41,42,43,45],c:[0,27,28,33],calcul:34,can:[22,33,36,38,39,44],cancer:[2,8,10,12,37,41,42,44,45],cart:[10,45],cell:44,center:34,central:[14,25,30,36,37,38],chain:[13,40],challeng:37,chang:[11,45],channel:33,chi:[1,33],choic:[41,42],choos:[2,40,41,42],cifar01:[4,43],classic:12,classif:[2,10,11,24,28,37,40,41,42,44,45],classifi:[9,37,45],clip:[2,41,42],cluster:15,cnn:[4,43],code:[1,2,3,6,10,12,13,14,15,22,24,28,33,34,35,36,37,38,39,40,41,42,43,45],coeffici:43,coin:45,collect:[2,4,40,41,42,43],combin:44,come:37,commun:33,compact:37,compar:[3,11,42,45],comparison:35,compet:[22,38,39],compil:43,complet:34,complex:[0,1,7,27,34,45],complic:[7,38,39],compon:12,comput:[10,38,39,45],computation:36,computerlab:33,con:[10,45],concept:30,condit:[35,36,37,38],confid:36,conjug:[14,38],connect:43,construct:[40,41,42],content:43,continu:39,contn:33,convex:[9,14,37,38],convolut:[4,13,39,40,43],convolution2dlay:43,correctli:[35,36],correl:[12,34,37,43,44],correspond:[37,38],cost:[2,11,24,34,35,36,37,38,40,41,42,43,45],cours:[25,32,33],covari:[6,12,30,34],cover:33,critic:28,cross:[7,27,36,37],ct:43,cumul:44,curv:44,cython:33,d:[0,27,28],data:[0,1,2,4,7,8,10,12,16,17,24,25,27,30,33,34,35,37,40,41,42,43,44,45],dataset:[2,4,40,41,42,43],deadlin:[0,27,28,33],decai:[3,38,39,42],decemb:0,decis:[10,11,45],decomposit:[6,12,18,26,34],deep:[2,3,37,41,42],defin:[0,2,33,40,41,42],definit:40,degre:[1,34],deliveri:[0,27,28],delta:36,demonstr:43,dens:[1,33,43],deriv:[6,13,34,35,36,37,38,40,41,42],descent:[3,11,14,22,28,37,38,39,42],descript:27,design:34,detail:[4,33,42,43],develop:[2,40,41,42],diagon:12,differ:[9,28,38,39,43],different:22,differenti:[0,3,14,38,39,42],diffus:[3,42],dimension:[3,4,9,24,27,34,42,43],directli:[38,39],disadvantag:[10,45],discret:30,discuss:[37,44],distribut:[6,30,35,36],distrubut:[35,36],doe:[34,35,39,40],domain:[30,43],don:43,dot:[38,39],down:[2,41,42,45],dropout:[2,41,42],e:[0,27,28],each:37,economi:34,effect:44,effici:43,electron:[0,27,28],element:[1,30,33,38,39],elimin:26,energi:33,ensembl:[11,45],entropi:[10,37],environ:[1,16,33],equat:[0,1,3,13,33,34,35,37,38,40,42],error:[1,11,33,34,36,45],essenti:33,estim:[35,36],et:22,etc:[33,43],euler:[3,42],evalu:[2,28,40,41,42,43],exampl:[1,2,3,4,5,7,8,9,10,11,22,33,34,35,36,37,38,39,40,41,42,43,44,45],exercis:[1,7,16,17,18,19,20,21,22,23,24,33,34,36,42],expect:[19,30,35,36],expens:36,experi:30,explod:44,explor:[1,16,17,33],exponenti:[3,42],express:[18,19,33,34,37,38],extend:[37,38],extrapol:[5,44],extrem:[11,33,45],ey:[11,45],f:[27,28],f_1:44,factor:45,fall:31,famili:[2,33,34,41,42],famou:26,fantast:34,featur:[10,26,34,45],feed:[2,13,39,40,41,42],file:43,find:[36,38,39,43],fine:[2,41,42],first:[5,13,28,33,34,35,37,38,40,42,45],fit:[1,11,33,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\ No newline at end of file diff --git a/doc/LectureNotes/_build/jupyter_execute/Project3.ipynb b/doc/LectureNotes/_build/jupyter_execute/Project3.ipynb index 7fec35ceb..93386990f 100644 --- a/doc/LectureNotes/_build/jupyter_execute/Project3.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/Project3.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "5831c36a", + "id": "32bbc99a", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "2d774c8b", + "id": "21397747", "metadata": { "editable": true }, @@ -22,14 +22,14 @@ "# Project 3 on Machine Learning, deadline December 18 (midnight), 2023\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 12, 2023**\n", + "Date: **Nov 13, 2023**\n", "\n", "Copyright 1999-2023, [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": "5cbde03b", + "id": "a13debcc", "metadata": { "editable": true }, @@ -39,7 +39,7 @@ }, { "cell_type": "markdown", - "id": "5170403b", + "id": "9d1f1220", "metadata": { "editable": true }, @@ -54,11 +54,15 @@ "3. Or other sources.\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**, 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. \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**, and **Decision Trees, Random Forests, Bagging and Boosting**.\n", + "\n", + "Feel also free to use support vector machines, $k$-means and principal components analysis, although the latter have not been covered during the lectures. This material can be found in the lecture notes.\n", + "\n", + "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. \n", "\n", "For Boosting, feel also free to write your own codes.\n", "\n", - "1. For project 3, you should feel free to use your own codes from projects 1 and 2, eventually write your own for Decision trees/random forests/bagging/boosting' or use the available functionality of **Scikit-Learn**, **Tensorflow**, etc. \n", + "1. For project 3, you should feel free to use your own codes from projects 1 and 2, eventually write your own for Decision trees/random forests/bagging/boosting' or use the available functionality of **Scikit-Learn**, **Tensorflow**, PyTorch etc. \n", "\n", "2. The estimates you used and tested in projects 1 and 2 should also be included, that is the $R2$-score, **MSE**, confusion matrix, accuracy score, information gain, ROC and Cumulative gains curves and other, cross-validation and/or bootstrap if these are relevant.\n", "\n", @@ -72,12 +76,12 @@ "\n", "We propose also an alternative to the above. This is a project on using machine learning methods (neural networks mainly) to the solution of ordinary differential equations and partial differential equations, with a final twist on how to diagonalize a symmetric matrix with neural networks.\n", "\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 43](https://compphysics.github.io/MachineLearning/doc/pub/week43/html/week42.html) and/or the textbook by [Yadav et al](https://www.springer.com/gp/book/9789401798150)." + "This is a field with large scientific interest, 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 43](https://compphysics.github.io/MachineLearning/doc/pub/week43/html/week42.html) and/or the textbook by [Yadav et al](https://www.springer.com/gp/book/9789401798150)." ] }, { "cell_type": "markdown", - "id": "e9fc8e8e", + "id": "6416060c", "metadata": { "editable": true }, @@ -89,7 +93,7 @@ }, { "cell_type": "markdown", - "id": "ce8b52a3", + "id": "18827262", "metadata": { "editable": true }, @@ -101,7 +105,7 @@ }, { "cell_type": "markdown", - "id": "eb943f7a", + "id": "bcee53f4", "metadata": { "editable": true }, @@ -113,7 +117,7 @@ }, { "cell_type": "markdown", - "id": "17447dcc", + "id": "83ec8275", "metadata": { "editable": true }, @@ -125,7 +129,7 @@ }, { "cell_type": "markdown", - "id": "d9bea856", + "id": "2be62c8e", "metadata": { "editable": true }, @@ -137,7 +141,7 @@ }, { "cell_type": "markdown", - "id": "5b74282b", + "id": "385e0b16", "metadata": { "editable": true }, @@ -149,7 +153,7 @@ }, { "cell_type": "markdown", - "id": "28f082d7", + "id": "fbf49165", "metadata": { "editable": true }, @@ -162,14 +166,27 @@ "equation in one dimension using a standard explicit scheme and neural\n", "networks to solve the same equations.\n", "\n", - "For the explicit scheme, you can study for example chapter 10 of the lecture notes in [Computational Physics](https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf) or alternative sources. For the solution of ordinary and partial differential equations using neural networks, the lectures by [Kristine Baluka Hein and included in the lectures of week 43](https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week43.html) at this course are highly recommended.\n", + "For the explicit scheme, you can study for example chapter 10 of the lecture notes in [Computational Physics, FYS3150/4150](https://github.com/CompPhysics/ComputationalPhysics/blob/master/doc/Lectures/lectures2015.pdf) or alternative sources from courses like [MAT-MEK4270](https://www.uio.no/studier/emner/matnat/math/MAT-MEK4270/index.html). For the solution of ordinary and partial differential equations using neural networks, the lectures by [included in the lectures of week 43](https://compphysics.github.io/MachineLearning/doc/pub/week42/html/week43.html) at this course are highly recommended.\n", "\n", - "For the machine learning part you can use your own code from project 2 or the functionality of for example **Tensorflow/Keras**.." + "For the machine learning part you can use your own code from project 2 or the functionality of for example **Tensorflow/Keras**, **PyTorch** or other libraries such [Physics informed machine learning](https://maziarraissi.github.io/PINNs/)." ] }, { "cell_type": "markdown", - "id": "5e855e7f", + "id": "c97df4f9", + "metadata": { + "editable": true + }, + "source": [ + "### Alternative differential equations\n", + "\n", + "Note that you can replace the one-dimensional diffusion equation discussed below with other sets of either ordinary differential equations or partial differential equations.\n", + "Please discuss such a change with us at the lab." + ] + }, + { + "cell_type": "markdown", + "id": "ecde0a0e", "metadata": { "editable": true }, @@ -183,7 +200,7 @@ }, { "cell_type": "markdown", - "id": "bc4be75f", + "id": "56429d7e", "metadata": { "editable": true }, @@ -195,7 +212,7 @@ }, { "cell_type": "markdown", - "id": "caa1eca4", + "id": "c2e49662", "metadata": { "editable": true }, @@ -205,7 +222,7 @@ }, { "cell_type": "markdown", - "id": "98a2bd5b", + "id": "fd661d63", "metadata": { "editable": true }, @@ -217,7 +234,7 @@ }, { "cell_type": "markdown", - "id": "980b949d", + "id": "a1d77183", "metadata": { "editable": true }, @@ -227,7 +244,7 @@ }, { "cell_type": "markdown", - "id": "7401e9ec", + "id": "73d187ef", "metadata": { "editable": true }, @@ -239,7 +256,7 @@ }, { "cell_type": "markdown", - "id": "e953662e", + "id": "b9b49da0", "metadata": { "editable": true }, @@ -250,7 +267,7 @@ }, { "cell_type": "markdown", - "id": "52122eb7", + "id": "aee685e3", "metadata": { "editable": true }, @@ -262,7 +279,7 @@ }, { "cell_type": "markdown", - "id": "ab71a383", + "id": "96ec18ae", "metadata": { "editable": true }, @@ -272,7 +289,7 @@ }, { "cell_type": "markdown", - "id": "4f886681", + "id": "57c542b7", "metadata": { "editable": true }, @@ -284,7 +301,7 @@ }, { "cell_type": "markdown", - "id": "83f8d12e", + "id": "b6e8e863", "metadata": { "editable": true }, @@ -297,7 +314,7 @@ }, { "cell_type": "markdown", - "id": "4bcf494f", + "id": "76ae9476", "metadata": { "editable": true }, @@ -309,7 +326,7 @@ }, { "cell_type": "markdown", - "id": "7f95982b", + "id": "95e239a3", "metadata": { "editable": true }, @@ -319,7 +336,7 @@ }, { "cell_type": "markdown", - "id": "1ee81ac0", + "id": "63fb1417", "metadata": { "editable": true }, @@ -331,7 +348,7 @@ }, { "cell_type": "markdown", - "id": "bae7898a", + "id": "e1c77709", "metadata": { "editable": true }, @@ -341,7 +358,7 @@ }, { "cell_type": "markdown", - "id": "5de23ea9", + "id": "2631e4bd", "metadata": { "editable": true }, @@ -353,7 +370,7 @@ }, { "cell_type": "markdown", - "id": "4d5d56e3", + "id": "0fa54ca3", "metadata": { "editable": true }, @@ -364,7 +381,7 @@ }, { "cell_type": "markdown", - "id": "e52523ef", + "id": "7579152d", "metadata": { "editable": true }, @@ -380,7 +397,7 @@ }, { "cell_type": "markdown", - "id": "678607d0", + "id": "e8ceb962", "metadata": { "editable": true }, @@ -397,21 +414,34 @@ }, { "cell_type": "markdown", - "id": "61d51d93", + "id": "fe5c3f2f", "metadata": { "editable": true }, "source": [ - "### Part d)\n", + "### Part d) Neural network complexity\n", "\n", - "Finally, present a critical assessment of the methods you have studied\n", - "and discuss the potential for the solving differential equations and\n", - "eigenvalue problems with machine learning methods." + "Here we study the stability of the results of the results as functions of the number of hidden nodes, layers and activation functions for the hidden layers.\n", + "Increase the number of hidden nodes and layers in order to see if this improves your results. Try also different activation functions for the hidden layers, such as the **tanh**, **ReLU**, and other activation functions. \n", + "Discuss your results." ] }, { "cell_type": "markdown", - "id": "d2163034", + "id": "3b2adf3a", + "metadata": { + "editable": true + }, + "source": [ + "### Part e)\n", + "\n", + "Finally, present a critical assessment of the methods you have studied\n", + "and discuss the potential for the solving differential equations with machine learning methods." + ] + }, + { + "cell_type": "markdown", + "id": "c2ed7243", "metadata": { "editable": true }, @@ -442,7 +472,7 @@ }, { "cell_type": "markdown", - "id": "e31290fd", + "id": "e9f450e7", "metadata": { "editable": true }, @@ -464,7 +494,7 @@ }, { "cell_type": "markdown", - "id": "99e78b73", + "id": "4ec24e55", "metadata": { "editable": true }, diff --git a/doc/LectureNotes/_build/jupyter_execute/project2.ipynb b/doc/LectureNotes/_build/jupyter_execute/project2.ipynb index 21bbab7b1..cbefb5694 100644 --- a/doc/LectureNotes/_build/jupyter_execute/project2.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/project2.ipynb @@ -2,34 +2,34 @@ "cells": [ { "cell_type": "markdown", - "id": "6afd3fdb", + "id": "515c9474", "metadata": { "editable": true }, "source": [ "\n", - "" + "" ] }, { "cell_type": "markdown", - "id": "21a10baa", + "id": "890dcb04", "metadata": { "editable": true }, "source": [ - "# Project 2 on Machine Learning, deadline November 13 (Midnight)\n", + "# Project 2 on Machine Learning, deadline November 17 (Midnight)\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: **Oct 9, 2023**\n", + "Date: **Nov 13, 2023**\n", "\n", "Copyright 1999-2023, [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": "4f67ce6e", + "id": "8cbf512f", "metadata": { "editable": true }, @@ -75,7 +75,7 @@ }, { "cell_type": "markdown", - "id": "67c6916e", + "id": "696379a0", "metadata": { "editable": true }, @@ -127,7 +127,7 @@ }, { "cell_type": "markdown", - "id": "fff54502", + "id": "81d48303", "metadata": { "editable": true }, @@ -166,7 +166,7 @@ }, { "cell_type": "markdown", - "id": "6e0f9012", + "id": "db6aebb0", "metadata": { "editable": true }, @@ -178,7 +178,7 @@ }, { "cell_type": "markdown", - "id": "075046bf", + "id": "18314d90", "metadata": { "editable": true }, @@ -204,7 +204,7 @@ }, { "cell_type": "markdown", - "id": "080dadf2", + "id": "dc3021aa", "metadata": { "editable": true }, @@ -216,7 +216,7 @@ }, { "cell_type": "markdown", - "id": "e3418dd4", + "id": "3c3c5b42", "metadata": { "editable": true }, @@ -236,7 +236,7 @@ }, { "cell_type": "markdown", - "id": "828ad79f", + "id": "1a493d07", "metadata": { "editable": true }, @@ -258,7 +258,7 @@ }, { "cell_type": "markdown", - "id": "07059668", + "id": "649a5380", "metadata": { "editable": true }, @@ -274,7 +274,7 @@ }, { "cell_type": "markdown", - "id": "e88200db", + "id": "804df082", "metadata": { "editable": true }, @@ -290,7 +290,7 @@ }, { "cell_type": "markdown", - "id": "66733be9", + "id": "2661bc83", "metadata": { "editable": true }, @@ -321,7 +321,7 @@ }, { "cell_type": "markdown", - "id": "5c49a400", + "id": "e651a157", "metadata": { "editable": true },