diff --git a/doc/Projects/2020/Project3/html/._Project3-bs000.html b/doc/Projects/2020/Project3/html/._Project3-bs000.html index 407048488..4d39c0544 100644 --- a/doc/Projects/2020/Project3/html/._Project3-bs000.html +++ b/doc/Projects/2020/Project3/html/._Project3-bs000.html @@ -156,7 +156,7 @@ MathJax.Hub.Config({
Department of Physics, University of Oslo, Norway

-

Nov 11, 2020

+

Dec 2, 2020


@@ -170,7 +170,7 @@ For project 3, you can propose own data sets that relate to your research intere

  1. Kaggle
    2. -
  2. The University of California at Irvine (UCI) with its machine learning repository
    3. +
  3. The University of California at Irvine (UCI) with its machine learning repository.
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: diff --git a/doc/Projects/2020/Project3/html/Project3-bs.html b/doc/Projects/2020/Project3/html/Project3-bs.html index 407048488..4d39c0544 100644 --- a/doc/Projects/2020/Project3/html/Project3-bs.html +++ b/doc/Projects/2020/Project3/html/Project3-bs.html @@ -156,7 +156,7 @@ MathJax.Hub.Config({
Department of Physics, University of Oslo, Norway

-

Nov 11, 2020

+

Dec 2, 2020


@@ -170,7 +170,7 @@ For project 3, you can propose own data sets that relate to your research intere

  1. Kaggle
    2. -
  2. The University of California at Irvine (UCI) with its machine learning repository
    3. +
  3. The University of California at Irvine (UCI) with its machine learning repository.
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: diff --git a/doc/Projects/2020/Project3/html/Project3.html b/doc/Projects/2020/Project3/html/Project3.html index 3ef33b972..4891999e7 100644 --- a/doc/Projects/2020/Project3/html/Project3.html +++ b/doc/Projects/2020/Project3/html/Project3.html @@ -107,7 +107,7 @@ MathJax.Hub.Config({
Department of Physics, University of Oslo, Norway

-

Nov 11, 2020

+

Dec 2, 2020


Paths for project 3

@@ -119,7 +119,7 @@ For project 3, you can propose own data sets that relate to your research intere
  1. Kaggle
    2. -
  2. The University of California at Irvine (UCI) with its machine learning repository
    3. +
  3. The University of California at Irvine (UCI) with its machine learning repository.
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: diff --git a/doc/Projects/2020/Project3/ipynb/Project3.ipynb b/doc/Projects/2020/Project3/ipynb/Project3.ipynb index d00e357db..c141abbf6 100644 --- a/doc/Projects/2020/Project3/ipynb/Project3.ipynb +++ b/doc/Projects/2020/Project3/ipynb/Project3.ipynb @@ -10,7 +10,7 @@ " \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 11, 2020**\n", + "Date: **Dec 2, 2020**\n", "\n", "Copyright 1999-2020, [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\n", "\n", @@ -24,7 +24,7 @@ "For project 3, you can propose own data sets that relate to your research interests or just use existing data sets from say\n", "1. [Kaggle](https://www.kaggle.com/datasets) \n", "\n", - "2. The [University of California at Irvine (UCI) with its machine learning repository](http://archive.ics.uci.edu/ml/datasets.html)\n", + "2. The [University of California at Irvine (UCI) with its machine learning repository](https://archive.ics.uci.edu/ml/index.php).\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**, **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. \n", diff --git a/doc/Projects/2020/Project3/ipynb/ipynb-Project3-src.tar.gz b/doc/Projects/2020/Project3/ipynb/ipynb-Project3-src.tar.gz index ea82d56ce..0e8e438e8 100644 Binary files a/doc/Projects/2020/Project3/ipynb/ipynb-Project3-src.tar.gz and b/doc/Projects/2020/Project3/ipynb/ipynb-Project3-src.tar.gz differ diff --git a/doc/Projects/2020/Project3/pdf/Project3.p.tex b/doc/Projects/2020/Project3/pdf/Project3.p.tex index c49762083..2e9bc905b 100644 --- a/doc/Projects/2020/Project3/pdf/Project3.p.tex +++ b/doc/Projects/2020/Project3/pdf/Project3.p.tex @@ -149,7 +149,7 @@ Project 3 on Machine Learning, deadline December 14 % --- begin date --- \begin{center} -Nov 11, 2020 +Dec 2, 2020 \end{center} % --- end date --- @@ -164,7 +164,7 @@ For project 3, you can propose own data sets that relate to your research intere \begin{enumerate} \item \href{{https://www.kaggle.com/datasets}}{Kaggle} -\item The \href{{http://archive.ics.uci.edu/ml/datasets.html}}{University of California at Irvine (UCI) with its machine learning repository} +\item The \href{{https://archive.ics.uci.edu/ml/index.php}}{University of California at Irvine (UCI) with its machine learning repository}. \end{enumerate} \noindent diff --git a/doc/Projects/2020/Project3/pdf/Project3.pdf b/doc/Projects/2020/Project3/pdf/Project3.pdf index 3b34403ff..e77cd2764 100644 Binary files a/doc/Projects/2020/Project3/pdf/Project3.pdf and b/doc/Projects/2020/Project3/pdf/Project3.pdf differ diff --git a/doc/Projects/2020/Project3/pdf/Project3.tex b/doc/Projects/2020/Project3/pdf/Project3.tex index 5bfb593fb..0ad2623df 100644 --- a/doc/Projects/2020/Project3/pdf/Project3.tex +++ b/doc/Projects/2020/Project3/pdf/Project3.tex @@ -123,7 +123,7 @@ Project 3 on Machine Learning, deadline December 14 % --- begin date --- \begin{center} -Nov 11, 2020 +Dec 2, 2020 \end{center} % --- end date --- @@ -138,7 +138,7 @@ For project 3, you can propose own data sets that relate to your research intere \begin{enumerate} \item \href{{https://www.kaggle.com/datasets}}{Kaggle} -\item The \href{{http://archive.ics.uci.edu/ml/datasets.html}}{University of California at Irvine (UCI) with its machine learning repository} +\item The \href{{https://archive.ics.uci.edu/ml/index.php}}{University of California at Irvine (UCI) with its machine learning repository}. \end{enumerate} \noindent diff --git a/doc/src/Projects/2020/Project3/Project3.do.txt b/doc/src/Projects/2020/Project3/Project3.do.txt index da45b94b9..c1a0423a9 100644 --- a/doc/src/Projects/2020/Project3/Project3.do.txt +++ b/doc/src/Projects/2020/Project3/Project3.do.txt @@ -9,7 +9,8 @@ DATE: today For project 3, you can propose own data sets that relate to your research interests or just use existing data sets from say o "Kaggle":"https://www.kaggle.com/datasets" -o The "University of California at Irvine (UCI) with its machine learning repository":"http://archive.ics.uci.edu/ml/datasets.html" +o The "University of California at Irvine (UCI) with its machine learning repository":"https://archive.ics.uci.edu/ml/index.php". + 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_, _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.