-
@@ -195,6 +195,9 @@ has been studied in the scientific literature. This makes it easier for you to compare and analyze your results. It is also an essential elements of the scientific discussion. +
+In particular, when developing your own Logistic Regression code for classification problems, the so-called Wisconsin Cancer data (which is a binary problem, benign or malignant tumors) may be studied. You find more information about this at the Scikit-Learn site or at the University of California at Irvine. The lecture slides on dimensionality reduction have several code examples on this data set. +
@@ -249,6 +252,15 @@ Regression code. You can compare your own results with those obtained using scikit-learn. +
+As stated in the introduction, it can also be useful to study other datasets. In particular, when developing your own Logistic Regression code for classification problems, the so-called \ +Wisconsin Cancer data (which is a binary problem, benign or malignant tumors) may be studied. You find more \ +information about this at the Scikit-Learn site or at the "University of California at Irvine":"https://archive.ics.uci.e\ +du/ml/datasets/breast+cancer+wisconsin+(original)". The lecture slides on dimensionality reduction have sev\ +eral code examples on this data set. +
diff --git a/doc/Projects/2019/Project2/html/Project2.html b/doc/Projects/2019/Project2/html/Project2.html index d83947f4f..e7fd8d5be 100644 --- a/doc/Projects/2019/Project2/html/Project2.html +++ b/doc/Projects/2019/Project2/html/Project2.html @@ -112,7 +112,7 @@ MathJax.Hub.Config({
-
+In particular, when developing your own Logistic Regression code for classification problems, the so-called Wisconsin Cancer data (which is a binary problem, benign or malignant tumors) may be studied. You find more information about this at the Scikit-Learn site or at the University of California at Irvine. The lecture slides on dimensionality reduction have several code examples on this data set. +
@@ -206,6 +209,15 @@ Regression code. You can compare your own results with those obtained using scikit-learn. +
+As stated in the introduction, it can also be useful to study other datasets. In particular, when developing your own Logistic Regression code for classification problems, the so-called \ +Wisconsin Cancer data (which is a binary problem, benign or malignant tumors) may be studied. You find more \ +information about this at the Scikit-Learn site or at the "University of California at Irvine":"https://archive.ics.uci.e\ +du/ml/datasets/breast+cancer+wisconsin+(original)". The lecture slides on dimensionality reduction have sev\ +eral code examples on this data set. +
diff --git a/doc/Projects/2019/Project2/ipynb/ipynb-Project2-src.tar.gz b/doc/Projects/2019/Project2/ipynb/ipynb-Project2-src.tar.gz index 837da8f94..4efdbdb1a 100644 Binary files a/doc/Projects/2019/Project2/ipynb/ipynb-Project2-src.tar.gz and b/doc/Projects/2019/Project2/ipynb/ipynb-Project2-src.tar.gz differ diff --git a/doc/Projects/2019/Project2/pdf/Project2.p.tex b/doc/Projects/2019/Project2/pdf/Project2.p.tex index 9e294cc54..53819c3fe 100644 --- a/doc/Projects/2019/Project2/pdf/Project2.p.tex +++ b/doc/Projects/2019/Project2/pdf/Project2.p.tex @@ -149,7 +149,7 @@ Project 2 on Machine Learning, deadline November 8 % --- begin date --- \begin{center} -Oct 9, 2019 +Oct 17, 2019 \end{center} % --- end date --- @@ -196,6 +196,8 @@ has been studied in the scientific literature. This makes it easier for you to compare and analyze your results. It is also an essential elements of the scientific discussion. +In particular, when developing your own Logistic Regression code for classification problems, the so-called Wisconsin Cancer data (which is a binary problem, benign or malignant tumors) may be studied. You find more information about this at the \href{{https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html}}{Scikit-Learn site} or at the \href{{https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)}}{University of California at Irvine}. The \href{{https://compphysics.github.io/MachineLearning/doc/pub/DimRed/html/DimRed.html}}{lecture slides on dimensionality reduction have several code examples on this data set}. + \paragraph{Part a): Write your Logistic Regression code, first step.} If you opt for the credit card data, your first task is to familiarize yourself with the data set and the scientific article. We recommend also that you study the code example in the \href{{https://compphysics.github.io/MachineLearning/doc/pub/LogReg/html/LogReg.html}}{Logistic Regression}. @@ -243,6 +245,15 @@ You can compare your own results with those obtained using \textbf{scikit-learn}. +As stated in the introduction, it can also be useful to study other datasets. In particular, when developing your own Logistic Regression code for classification problems, the so-called \ +Wisconsin Cancer data (which is a binary problem, benign or malignant tumors) may be studied. You find more \ +information about this at the \href{{https://scikit-learn.org/stable/modules/generated/sklearn\ +.datasets.load_breast_cancer.html}}{Scikit-Learn site} or at the "University of California at Irvine":"https://archive.ics.uci.e\ +du/ml/datasets/breast+cancer+wisconsin+(original)". The \href{{https://compphysics.github.io/MachineLearning/doc/pub/DimRed/html/DimR\ +ed.html}}{lecture slides on dimensionality reduction have sev\ +eral code examples on this data set}. + + \paragraph{Part c): Writing your own Neural Network code.} Your aim now, and this is the central part of this project, is to write to your own Feed Forward Neural Network code implementing the back diff --git a/doc/Projects/2019/Project2/pdf/Project2.pdf b/doc/Projects/2019/Project2/pdf/Project2.pdf index 98f2236ff..60975a89a 100644 Binary files a/doc/Projects/2019/Project2/pdf/Project2.pdf and b/doc/Projects/2019/Project2/pdf/Project2.pdf differ diff --git a/doc/Projects/2019/Project2/pdf/Project2.tex b/doc/Projects/2019/Project2/pdf/Project2.tex index 827012c40..535eea32f 100644 --- a/doc/Projects/2019/Project2/pdf/Project2.tex +++ b/doc/Projects/2019/Project2/pdf/Project2.tex @@ -123,7 +123,7 @@ Project 2 on Machine Learning, deadline November 8 % --- begin date --- \begin{center} -Oct 9, 2019 +Oct 17, 2019 \end{center} % --- end date --- @@ -170,6 +170,8 @@ has been studied in the scientific literature. This makes it easier for you to compare and analyze your results. It is also an essential elements of the scientific discussion. +In particular, when developing your own Logistic Regression code for classification problems, the so-called Wisconsin Cancer data (which is a binary problem, benign or malignant tumors) may be studied. You find more information about this at the \href{{https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html}}{Scikit-Learn site} or at the \href{{https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)}}{University of California at Irvine}. The \href{{https://compphysics.github.io/MachineLearning/doc/pub/DimRed/html/DimRed.html}}{lecture slides on dimensionality reduction have several code examples on this data set}. + \paragraph{Part a): Write your Logistic Regression code, first step.} If you opt for the credit card data, your first task is to familiarize yourself with the data set and the scientific article. We recommend also that you study the code example in the \href{{https://compphysics.github.io/MachineLearning/doc/pub/LogReg/html/LogReg.html}}{Logistic Regression}. @@ -217,6 +219,15 @@ You can compare your own results with those obtained using \textbf{scikit-learn}. +As stated in the introduction, it can also be useful to study other datasets. In particular, when developing your own Logistic Regression code for classification problems, the so-called \ +Wisconsin Cancer data (which is a binary problem, benign or malignant tumors) may be studied. You find more \ +information about this at the \href{{https://scikit-learn.org/stable/modules/generated/sklearn\ +.datasets.load_breast_cancer.html}}{Scikit-Learn site} or at the "University of California at Irvine":"https://archive.ics.uci.e\ +du/ml/datasets/breast+cancer+wisconsin+(original)". The \href{{https://compphysics.github.io/MachineLearning/doc/pub/DimRed/html/DimR\ +ed.html}}{lecture slides on dimensionality reduction have sev\ +eral code examples on this data set}. + + \paragraph{Part c): Writing your own Neural Network code.} Your aim now, and this is the central part of this project, is to write to your own Feed Forward Neural Network code implementing the back diff --git a/doc/src/Projects/2019/Project2/Project2.do.txt b/doc/src/Projects/2019/Project2/Project2.do.txt index fc7ac287b..882eeda38 100644 --- a/doc/src/Projects/2019/Project2/Project2.do.txt +++ b/doc/src/Projects/2019/Project2/Project2.do.txt @@ -22,6 +22,7 @@ The data sets that we propose here are (the default sets) * Classification. Here you will also need to develop a Logistic regression code that you will use to compare with the Neural Network code. The data set we propose are o The credit card data set from "UCI":"https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients". This data set links to a "recent scientific article":"https://bradzzz.gitbooks.io/ga-seattle-dsi/content/dsi/dsi_05_classification_databases/2.1-lesson/assets/datasets/DefaultCreditCardClients_yeh_2009.pdf". Furthermore, in the lecture slides on "Logistic Regression":"https://compphysics.github.io/MachineLearning/doc/pub/LogReg/html/LogReg.html", you will find an example code for the Credit Card data. You could use this code as an example on how to read the data and use _Scikit-Learn_ to run a classification problem. + However, if you would like to study other data sets, feel free to propose other sets. What we listed here are mere suggestions from our side. If you opt for another data set, consider using a set which @@ -29,6 +30,8 @@ has been studied in the scientific literature. This makes it easier for you to compare and analyze your results. It is also an essential elements of the scientific discussion. +In particular, when developing your own Logistic Regression code for classification problems, the so-called Wisconsin Cancer data (which is a binary problem, benign or malignant tumors) may be studied. You find more information about this at the "Scikit-Learn site":"https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html" or at the "University of California at Irvine":"https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)". The "lecture slides on dimensionality reduction have several code examples on this data set":"https://compphysics.github.io/MachineLearning/doc/pub/DimRed/html/DimRed.html". + === Part a): Write your Logistic Regression code, first step === If you opt for the credit card data, your first task is to familiarize yourself with the data set and the scientific article. @@ -80,6 +83,15 @@ You can compare your own results with those obtained using _scikit-learn_. +As stated in the introduction, it can also be useful to study other datasets. In particular, when developing your own Logistic Regression code for classification problems, the so-called \ +Wisconsin Cancer data (which is a binary problem, benign or malignant tumors) may be studied. You find more \ +information about this at the "Scikit-Learn site":"https://scikit-learn.org/stable/modules/generated/sklearn\ +.datasets.load_breast_cancer.html" or at the "University of California at Irvine":"https://archive.ics.uci.e\ +du/ml/datasets/breast+cancer+wisconsin+(original)". The "lecture slides on dimensionality reduction have sev\ +eral code examples on this data set":"https://compphysics.github.io/MachineLearning/doc/pub/DimRed/html/DimR\ +ed.html". + + === Part c): Writing your own Neural Network code === Your aim now, and this is the central part of this project, is to