The main aim of this project is to study in more detail various
-regression methods, including the Ordinary Least Squares (OLS) method,
+regression methods, including the Ordinary Least Squares (OLS) method.
In addition to the scientific part, in this course we want also to
give you an experience in writing scientific reports. The format for
the delivery of your answers is namely that of a scientific report. At
@@ -189,7 +189,7 @@ recommend to do the code development and testing with a simpler
one-dimensional function, similar to those discussed in the exercises
of week 35. A simple test, as discussed during the lectures the first
two weeks is to set the design matrix equal to the identity
-matrix. Then our model should give a mean square error which is exactly equal to zero.
+matrix. Then your model should give a mean square error which is exactly equal to zero.
When you are sure that your codes function well, you can then replace
the one-dimensional test function with the two-dimensional Franke function
discussed here.
diff --git a/doc/Projects/2022/Project1/html/Project1-bs.html b/doc/Projects/2022/Project1/html/Project1-bs.html
index 27a4b8847..affba1fe4 100644
--- a/doc/Projects/2022/Project1/html/Project1-bs.html
+++ b/doc/Projects/2022/Project1/html/Project1-bs.html
@@ -162,7 +162,7 @@ MathJax.Hub.Config({
The main aim of this project is to study in more detail various
-regression methods, including the Ordinary Least Squares (OLS) method,
+regression methods, including the Ordinary Least Squares (OLS) method.
In addition to the scientific part, in this course we want also to
give you an experience in writing scientific reports. The format for
the delivery of your answers is namely that of a scientific report. At
@@ -189,7 +189,7 @@ recommend to do the code development and testing with a simpler
one-dimensional function, similar to those discussed in the exercises
of week 35. A simple test, as discussed during the lectures the first
two weeks is to set the design matrix equal to the identity
-matrix. Then our model should give a mean square error which is exactly equal to zero.
+matrix. Then your model should give a mean square error which is exactly equal to zero.
When you are sure that your codes function well, you can then replace
the one-dimensional test function with the two-dimensional Franke function
discussed here.
diff --git a/doc/Projects/2022/Project1/html/Project1.html b/doc/Projects/2022/Project1/html/Project1.html
index e62ff2afa..3230fa730 100644
--- a/doc/Projects/2022/Project1/html/Project1.html
+++ b/doc/Projects/2022/Project1/html/Project1.html
@@ -201,13 +201,13 @@ MathJax.Hub.Config({
The main aim of this project is to study in more detail various -regression methods, including the Ordinary Least Squares (OLS) method, +regression methods, including the Ordinary Least Squares (OLS) method. In addition to the scientific part, in this course we want also to give you an experience in writing scientific reports. The format for the delivery of your answers is namely that of a scientific report. At @@ -225,7 +225,7 @@ recommend to do the code development and testing with a simpler one-dimensional function, similar to those discussed in the exercises of week 35. A simple test, as discussed during the lectures the first two weeks is to set the design matrix equal to the identity -matrix. Then our model should give a mean square error which is exactly equal to zero. +matrix. Then your model should give a mean square error which is exactly equal to zero. When you are sure that your codes function well, you can then replace the one-dimensional test function with the two-dimensional Franke function discussed here. diff --git a/doc/Projects/2022/Project1/ipynb/Project1.ipynb b/doc/Projects/2022/Project1/ipynb/Project1.ipynb index e45c271d8..3af46c0ae 100644 --- a/doc/Projects/2022/Project1/ipynb/Project1.ipynb +++ b/doc/Projects/2022/Project1/ipynb/Project1.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "7a2ac097", + "id": "f3a8c4d6", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "4c81032a", + "id": "ba2f6e4e", "metadata": { "editable": true }, @@ -22,12 +22,12 @@ "# Project 1 on Machine Learning, deadline October 7, 2022\n", "**[Data Analysis and Machine Learning FYS-STK3155/FYS4155](http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html)**, University of Oslo, Norway\n", "\n", - "Date: **Sep 5, 2022**" + "Date: **Sep 6, 2022**" ] }, { "cell_type": "markdown", - "id": "3a6a8595", + "id": "e7d1ae4f", "metadata": { "editable": true }, @@ -35,7 +35,7 @@ "## Regression analysis and resampling methods\n", "\n", "The main aim of this project is to study in more detail various\n", - "regression methods, including the Ordinary Least Squares (OLS) method,\n", + "regression methods, including the Ordinary Least Squares (OLS) method.\n", "In addition to the scientific part, in this course we want also to\n", "give you an experience in writing scientific reports. The format for\n", "the delivery of your answers is namely that of a scientific report. At\n", @@ -52,7 +52,7 @@ "one-dimensional function, similar to those discussed in the exercises\n", "of week 35. A simple test, as discussed during the lectures the first\n", "two weeks is to set the design matrix equal to the identity\n", - "matrix. Then our model should give a mean square error which is exactly equal to zero.\n", + "matrix. Then your model should give a mean square error which is exactly equal to zero.\n", "When you are sure that your codes function well, you can then replace\n", "the one-dimensional test function with the two-dimensional **Franke** function\n", "discussed here.\n", @@ -63,7 +63,7 @@ }, { "cell_type": "markdown", - "id": "612a16af", + "id": "8524af72", "metadata": { "editable": true }, @@ -85,7 +85,7 @@ }, { "cell_type": "markdown", - "id": "9acb8af9", + "id": "67acaf3f", "metadata": { "editable": true }, @@ -100,7 +100,7 @@ }, { "cell_type": "markdown", - "id": "1760b791", + "id": "7fbfb83f", "metadata": { "editable": true }, @@ -129,7 +129,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "0887069d", + "id": "365c9b12", "metadata": { "collapsed": false, "editable": true @@ -181,7 +181,7 @@ }, { "cell_type": "markdown", - "id": "6ba1eed9", + "id": "42bf4d8c", "metadata": { "editable": true }, @@ -197,7 +197,7 @@ }, { "cell_type": "markdown", - "id": "b8977dc1", + "id": "1a97a8a9", "metadata": { "editable": true }, @@ -209,7 +209,7 @@ }, { "cell_type": "markdown", - "id": "97dbce3f", + "id": "005a34cd", "metadata": { "editable": true }, @@ -220,7 +220,7 @@ }, { "cell_type": "markdown", - "id": "918c0cbb", + "id": "f5cba6b7", "metadata": { "editable": true }, @@ -232,7 +232,7 @@ }, { "cell_type": "markdown", - "id": "12ed8d76", + "id": "c614fcd7", "metadata": { "editable": true }, @@ -244,7 +244,7 @@ }, { "cell_type": "markdown", - "id": "dde5fd1a", + "id": "d05b6889", "metadata": { "editable": true }, @@ -256,7 +256,7 @@ }, { "cell_type": "markdown", - "id": "5ab0b041", + "id": "16138919", "metadata": { "editable": true }, @@ -267,7 +267,7 @@ }, { "cell_type": "markdown", - "id": "d10e8fa5", + "id": "bc66b35b", "metadata": { "editable": true }, @@ -279,7 +279,7 @@ }, { "cell_type": "markdown", - "id": "4bde13fb", + "id": "4eb37340", "metadata": { "editable": true }, @@ -292,7 +292,7 @@ }, { "cell_type": "markdown", - "id": "8635bca3", + "id": "325b1020", "metadata": { "editable": true }, @@ -304,7 +304,7 @@ }, { "cell_type": "markdown", - "id": "da21e635", + "id": "df636f58", "metadata": { "editable": true }, @@ -314,7 +314,7 @@ }, { "cell_type": "markdown", - "id": "62ce1847", + "id": "7362a90c", "metadata": { "editable": true }, @@ -326,7 +326,7 @@ }, { "cell_type": "markdown", - "id": "8b985826", + "id": "4a23d9aa", "metadata": { "editable": true }, @@ -337,7 +337,7 @@ }, { "cell_type": "markdown", - "id": "1c015e08", + "id": "048427bb", "metadata": { "editable": true }, @@ -359,7 +359,7 @@ }, { "cell_type": "markdown", - "id": "ea1b5d0b", + "id": "5798da61", "metadata": { "editable": true }, @@ -372,7 +372,7 @@ }, { "cell_type": "markdown", - "id": "362e556a", + "id": "dabdfbc1", "metadata": { "editable": true }, @@ -384,7 +384,7 @@ }, { "cell_type": "markdown", - "id": "e5eeda54", + "id": "6f83ef69", "metadata": { "editable": true }, @@ -396,7 +396,7 @@ }, { "cell_type": "markdown", - "id": "70012504", + "id": "fd117767", "metadata": { "editable": true }, @@ -406,7 +406,7 @@ }, { "cell_type": "markdown", - "id": "8d234f27", + "id": "36d8ba67", "metadata": { "editable": true }, @@ -418,7 +418,7 @@ }, { "cell_type": "markdown", - "id": "3460f330", + "id": "277e8cbd", "metadata": { "editable": true }, @@ -447,7 +447,7 @@ }, { "cell_type": "markdown", - "id": "2b0214ff", + "id": "e0056ae2", "metadata": { "editable": true }, @@ -479,7 +479,7 @@ }, { "cell_type": "markdown", - "id": "ba76c42f", + "id": "5bea6c04", "metadata": { "editable": true }, @@ -491,7 +491,7 @@ }, { "cell_type": "markdown", - "id": "cc9118d1", + "id": "2b233d93", "metadata": { "editable": true }, @@ -510,7 +510,7 @@ }, { "cell_type": "markdown", - "id": "f0901443", + "id": "bbfe79b1", "metadata": { "editable": true }, @@ -522,7 +522,7 @@ }, { "cell_type": "markdown", - "id": "128dfd18", + "id": "f6b6ef4e", "metadata": { "editable": true }, @@ -536,7 +536,7 @@ }, { "cell_type": "markdown", - "id": "e5ff09e8", + "id": "d42ac463", "metadata": { "editable": true }, @@ -548,7 +548,7 @@ }, { "cell_type": "markdown", - "id": "a39514b2", + "id": "93d7fb47", "metadata": { "editable": true }, @@ -558,7 +558,7 @@ }, { "cell_type": "markdown", - "id": "4c34a548", + "id": "9e03ac19", "metadata": { "editable": true }, @@ -570,7 +570,7 @@ }, { "cell_type": "markdown", - "id": "300bde6a", + "id": "c1a62cd8", "metadata": { "editable": true }, @@ -580,7 +580,7 @@ }, { "cell_type": "markdown", - "id": "71a2c51a", + "id": "2ceb482f", "metadata": { "editable": true }, @@ -592,7 +592,7 @@ }, { "cell_type": "markdown", - "id": "f7e980d0", + "id": "61d409d6", "metadata": { "editable": true }, @@ -611,7 +611,7 @@ }, { "cell_type": "markdown", - "id": "ef87f011", + "id": "d5b3b111", "metadata": { "editable": true }, @@ -636,7 +636,7 @@ }, { "cell_type": "markdown", - "id": "1288c49d", + "id": "19411bb1", "metadata": { "editable": true }, @@ -656,7 +656,7 @@ }, { "cell_type": "markdown", - "id": "8ba58fed", + "id": "33c43b4e", "metadata": { "editable": true }, @@ -673,7 +673,7 @@ }, { "cell_type": "markdown", - "id": "4b4db7cc", + "id": "1daf2ad3", "metadata": { "editable": true }, @@ -701,7 +701,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "daf3d605", + "id": "26959ad8", "metadata": { "collapsed": false, "editable": true @@ -713,7 +713,7 @@ }, { "cell_type": "markdown", - "id": "25e4e3c2", + "id": "4cb0aeb9", "metadata": { "editable": true }, @@ -725,7 +725,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "045af476", + "id": "15a9a7ad", "metadata": { "collapsed": false, "editable": true @@ -751,7 +751,7 @@ }, { "cell_type": "markdown", - "id": "44ca47a4", + "id": "1ca366f6", "metadata": { "editable": true }, @@ -776,7 +776,7 @@ }, { "cell_type": "markdown", - "id": "0bff19b9", + "id": "ed16fcae", "metadata": { "editable": true }, @@ -790,7 +790,7 @@ }, { "cell_type": "markdown", - "id": "f244210d", + "id": "a10f8033", "metadata": { "editable": true }, @@ -820,7 +820,7 @@ }, { "cell_type": "markdown", - "id": "a44b37da", + "id": "04989dae", "metadata": { "editable": true }, @@ -842,7 +842,7 @@ }, { "cell_type": "markdown", - "id": "36139a4b", + "id": "df183f51", "metadata": { "editable": true }, diff --git a/doc/Projects/2022/Project1/ipynb/ipynb-Project1-src.tar.gz b/doc/Projects/2022/Project1/ipynb/ipynb-Project1-src.tar.gz index 54c7d870b..68e17223f 100644 Binary files a/doc/Projects/2022/Project1/ipynb/ipynb-Project1-src.tar.gz and b/doc/Projects/2022/Project1/ipynb/ipynb-Project1-src.tar.gz differ diff --git a/doc/Projects/2022/Project1/pdf/Project1.p.tex b/doc/Projects/2022/Project1/pdf/Project1.p.tex index 2a080cd07..37f743039 100644 --- a/doc/Projects/2022/Project1/pdf/Project1.p.tex +++ b/doc/Projects/2022/Project1/pdf/Project1.p.tex @@ -132,7 +132,7 @@ Project 1 on Machine Learning, deadline October 7, 2022 % --- begin date --- \begin{center} -Sep 5, 2022 +Sep 6, 2022 \end{center} % --- end date --- @@ -142,7 +142,7 @@ Sep 5, 2022 \subsection{Regression analysis and resampling methods} The main aim of this project is to study in more detail various -regression methods, including the Ordinary Least Squares (OLS) method, +regression methods, including the Ordinary Least Squares (OLS) method. In addition to the scientific part, in this course we want also to give you an experience in writing scientific reports. The format for the delivery of your answers is namely that of a scientific report. At @@ -159,7 +159,7 @@ recommend to do the code development and testing with a simpler one-dimensional function, similar to those discussed in the exercises of week 35. A simple test, as discussed during the lectures the first two weeks is to set the design matrix equal to the identity -matrix. Then our model should give a mean square error which is exactly equal to zero. +matrix. Then your model should give a mean square error which is exactly equal to zero. When you are sure that your codes function well, you can then replace the one-dimensional test function with the two-dimensional \textbf{Franke} function discussed here. diff --git a/doc/Projects/2022/Project1/pdf/Project1.pdf b/doc/Projects/2022/Project1/pdf/Project1.pdf index c39dde934..19452bc03 100644 Binary files a/doc/Projects/2022/Project1/pdf/Project1.pdf and b/doc/Projects/2022/Project1/pdf/Project1.pdf differ diff --git a/doc/Projects/2022/Project1/pdf/Project1.tex b/doc/Projects/2022/Project1/pdf/Project1.tex index c7a997eb9..f75247234 100644 --- a/doc/Projects/2022/Project1/pdf/Project1.tex +++ b/doc/Projects/2022/Project1/pdf/Project1.tex @@ -102,7 +102,7 @@ Project 1 on Machine Learning, deadline October 7, 2022 % --- begin date --- \begin{center} -Sep 5, 2022 +Sep 6, 2022 \end{center} % --- end date --- @@ -112,7 +112,7 @@ Sep 5, 2022 \subsection*{Regression analysis and resampling methods} The main aim of this project is to study in more detail various -regression methods, including the Ordinary Least Squares (OLS) method, +regression methods, including the Ordinary Least Squares (OLS) method. In addition to the scientific part, in this course we want also to give you an experience in writing scientific reports. The format for the delivery of your answers is namely that of a scientific report. At @@ -129,7 +129,7 @@ recommend to do the code development and testing with a simpler one-dimensional function, similar to those discussed in the exercises of week 35. A simple test, as discussed during the lectures the first two weeks is to set the design matrix equal to the identity -matrix. Then our model should give a mean square error which is exactly equal to zero. +matrix. Then your model should give a mean square error which is exactly equal to zero. When you are sure that your codes function well, you can then replace the one-dimensional test function with the two-dimensional \textbf{Franke} function discussed here. diff --git a/doc/src/Projects/2022/Project1/Project1.do.txt b/doc/src/Projects/2022/Project1/Project1.do.txt index 02837463c..3aa99f31c 100644 --- a/doc/src/Projects/2022/Project1/Project1.do.txt +++ b/doc/src/Projects/2022/Project1/Project1.do.txt @@ -9,7 +9,7 @@ DATE: today The main aim of this project is to study in more detail various -regression methods, including the Ordinary Least Squares (OLS) method, +regression methods, including the Ordinary Least Squares (OLS) method. In addition to the scientific part, in this course we want also to give you an experience in writing scientific reports. The format for the delivery of your answers is namely that of a scientific report. At @@ -27,7 +27,7 @@ recommend to do the code development and testing with a simpler one-dimensional function, similar to those discussed in the exercises of week 35. A simple test, as discussed during the lectures the first two weeks is to set the design matrix equal to the identity -matrix. Then our model should give a mean square error which is exactly equal to zero. +matrix. Then your model should give a mean square error which is exactly equal to zero. When you are sure that your codes function well, you can then replace the one-dimensional test function with the two-dimensional _Franke_ function discussed here.