diff --git a/doc/Projects/2018/Project1/html/._Project1-bs000.html b/doc/Projects/2018/Project1/html/._Project1-bs000.html index 4af3b425e..86a318ab3 100644 --- a/doc/Projects/2018/Project1/html/._Project1-bs000.html +++ b/doc/Projects/2018/Project1/html/._Project1-bs000.html @@ -151,7 +151,7 @@ MathJax.Hub.Config({
Department of Physics, University of Oslo, Norway

-

Sep 12, 2018

+

Sep 14, 2018


@@ -171,7 +171,7 @@ to fit polynomials to a specific two-dimensional function called function. This is a function which has been widely used when testing various interpolation and fitting algorithms. Furthermore, after having etsablished the model and the -method, we will employ resamling techniques such as the cross-validation and +method, we will employ resamling techniques such as the cross-validation and/or the bootstrap methods, in order to perform a proper assessment of our models.

@@ -276,8 +276,8 @@ $$ $$

-Perform a resampling of the data where you split the data in training data and test data. Implement the \( k \)-fold cross-validation algorithm and the bootstrap algorithm -and evaluate again the MSE and the \( R^2 \) functions resulting from the test data. +Perform a resampling of the data where you split the data in training data and test data. Implement the \( k \)-fold cross-validation algorithm and/or the bootstrap algorithm +and evaluate again the MSE and the \( R^2 \) functions resulting from the test data. Evaluate also the bias and variance of the final models using for example equation (7.9) in the textbook of Hastie et al.

Part b): Ridge Regression on the Franke function with resampling

diff --git a/doc/Projects/2018/Project1/html/Project1-bs.html b/doc/Projects/2018/Project1/html/Project1-bs.html index 4af3b425e..86a318ab3 100644 --- a/doc/Projects/2018/Project1/html/Project1-bs.html +++ b/doc/Projects/2018/Project1/html/Project1-bs.html @@ -151,7 +151,7 @@ MathJax.Hub.Config({
Department of Physics, University of Oslo, Norway

-

Sep 12, 2018

+

Sep 14, 2018


@@ -171,7 +171,7 @@ to fit polynomials to a specific two-dimensional function called function. This is a function which has been widely used when testing various interpolation and fitting algorithms. Furthermore, after having etsablished the model and the -method, we will employ resamling techniques such as the cross-validation and +method, we will employ resamling techniques such as the cross-validation and/or the bootstrap methods, in order to perform a proper assessment of our models.

@@ -276,8 +276,8 @@ $$ $$

-Perform a resampling of the data where you split the data in training data and test data. Implement the \( k \)-fold cross-validation algorithm and the bootstrap algorithm -and evaluate again the MSE and the \( R^2 \) functions resulting from the test data. +Perform a resampling of the data where you split the data in training data and test data. Implement the \( k \)-fold cross-validation algorithm and/or the bootstrap algorithm +and evaluate again the MSE and the \( R^2 \) functions resulting from the test data. Evaluate also the bias and variance of the final models using for example equation (7.9) in the textbook of Hastie et al.

Part b): Ridge Regression on the Franke function with resampling

diff --git a/doc/Projects/2018/Project1/html/Project1.html b/doc/Projects/2018/Project1/html/Project1.html index d6d34823c..b4daafe6a 100644 --- a/doc/Projects/2018/Project1/html/Project1.html +++ b/doc/Projects/2018/Project1/html/Project1.html @@ -110,7 +110,7 @@ MathJax.Hub.Config({
Department of Physics, University of Oslo, Norway

-

Sep 12, 2018

+

Sep 14, 2018


Regression analysis and resampling methods

@@ -128,7 +128,7 @@ to fit polynomials to a specific two-dimensional function called function. This is a function which has been widely used when testing various interpolation and fitting algorithms. Furthermore, after having etsablished the model and the -method, we will employ resamling techniques such as the cross-validation and +method, we will employ resamling techniques such as the cross-validation and/or the bootstrap methods, in order to perform a proper assessment of our models.

@@ -233,8 +233,8 @@ $$ $$

-Perform a resampling of the data where you split the data in training data and test data. Implement the \( k \)-fold cross-validation algorithm and the bootstrap algorithm -and evaluate again the MSE and the \( R^2 \) functions resulting from the test data. +Perform a resampling of the data where you split the data in training data and test data. Implement the \( k \)-fold cross-validation algorithm and/or the bootstrap algorithm +and evaluate again the MSE and the \( R^2 \) functions resulting from the test data. Evaluate also the bias and variance of the final models using for example equation (7.9) in the textbook of Hastie et al.

Part b): Ridge Regression on the Franke function with resampling

diff --git a/doc/Projects/2018/Project1/ipynb/ipynb-Project1-src.tar.gz b/doc/Projects/2018/Project1/ipynb/ipynb-Project1-src.tar.gz index 87a501d0c..00aaa3a6b 100644 Binary files a/doc/Projects/2018/Project1/ipynb/ipynb-Project1-src.tar.gz and b/doc/Projects/2018/Project1/ipynb/ipynb-Project1-src.tar.gz differ diff --git a/doc/Projects/2018/Project1/pdf/Project1.pdf b/doc/Projects/2018/Project1/pdf/Project1.pdf index 2a91716cf..c53ff5178 100644 Binary files a/doc/Projects/2018/Project1/pdf/Project1.pdf and b/doc/Projects/2018/Project1/pdf/Project1.pdf differ diff --git a/doc/Projects/2018/Project1/pdf/Project1.tex b/doc/Projects/2018/Project1/pdf/Project1.tex index 5beb940f3..1f0103ef7 100644 --- a/doc/Projects/2018/Project1/pdf/Project1.tex +++ b/doc/Projects/2018/Project1/pdf/Project1.tex @@ -125,7 +125,7 @@ Project 1 on Machine Learning, deadline October 1 % --- begin date --- \begin{center} -Sep 12, 2018 +Sep 14, 2018 \end{center} % --- end date --- @@ -145,7 +145,7 @@ to fit polynomials to a specific two-dimensional function called function}. This is a function which has been widely used when testing various interpolation and fitting algorithms. Furthermore, after having etsablished the model and the -method, we will employ resamling techniques such as the cross-validation and +method, we will employ resamling techniques such as the cross-validation and/or the bootstrap methods, in order to perform a proper assessment of our models. @@ -240,8 +240,8 @@ where we have defined the mean value of $\hat{y}$ as \bar{y} = \frac{1}{n} \sum_{i=0}^{n - 1} y_i. \] -Perform a resampling of the data where you split the data in training data and test data. Implement the $k$-fold cross-validation algorithm and the bootstrap algorithm -and evaluate again the MSE and the $R^2$ functions resulting from the test data. +Perform a resampling of the data where you split the data in training data and test data. Implement the $k$-fold cross-validation algorithm and/or the bootstrap algorithm +and evaluate again the MSE and the $R^2$ functions resulting from the test data. Evaluate also the bias and variance of the final models using for example equation (7.9) in the textbook of Hastie \emph{et al.} @@ -258,7 +258,7 @@ We are going to download digital terrain data from the website \href{{https://ea In order to obtain data for a specific region, you need to register as a user (free) at this website and then decide upon which area you want to fetch the digital terrain data from. In order to be able to read the data properly, you need to specify that the format should be \textbf{SRTM Arc-Second Global} and download the data as a \textbf{GeoTIF} file. The files are then stored in \emph{tif} format which can be imported into a Python program using \begin{verbatim} -from imageio import imread +scipy.misc.imread \end{verbatim} Here is a simple part of a Python code which reads and plots the data from such files diff --git a/doc/src/Projects/2018/Project1/Project1.do.txt b/doc/src/Projects/2018/Project1/Project1.do.txt index 5dd7a086b..66e3c7e77 100644 --- a/doc/src/Projects/2018/Project1/Project1.do.txt +++ b/doc/src/Projects/2018/Project1/Project1.do.txt @@ -16,7 +16,7 @@ to fit polynomials to a specific two-dimensional function called function":"http://www.dtic.mil/dtic/tr/fulltext/u2/a081688.pdf". This is a function which has been widely used when testing various interpolation and fitting algorithms. Furthermore, after having etsablished the model and the -method, we will employ resamling techniques such as the cross-validation and +method, we will employ resamling techniques such as the cross-validation and/or the bootstrap methods, in order to perform a proper assessment of our models. @@ -120,8 +120,8 @@ where we have defined the mean value of $\hat{y}$ as \] !et -Perform a resampling of the data where you split the data in training data and test data. Implement the $k$-fold cross-validation algorithm and the bootstrap algorithm -and evaluate again the MSE and the $R^2$ functions resulting from the test data. +Perform a resampling of the data where you split the data in training data and test data. Implement the $k$-fold cross-validation algorithm and/or the bootstrap algorithm +and evaluate again the MSE and the $R^2$ functions resulting from the test data. Evaluate also the bias and variance of the final models using for example equation (7.9) in the textbook of Hastie *et al.*