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cleaning up typos

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This commit is contained in:
Morten Hjorth-Jensen
2022-09-06 09:00:52 +02:00
parent 7c16c4386f
commit 997118dd68
9 changed files with 70 additions and 70 deletions
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doc/Projects/2022/Project1/html/._Project1-bs000.html
+3 -3
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@@ -162,7 +162,7 @@ MathJax.Hub.Config({
</center>
<br>
<center>
<h4>Sep 5, 2022</h4>
<h4>Sep 6, 2022</h4>
</center> <!-- date -->
<br>
@@ -171,7 +171,7 @@ MathJax.Hub.Config({
<h2 id="regression-analysis-and-resampling-methods" class="anchor">Regression analysis and resampling methods </h2>
<p>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 <b>Franke</b> function
discussed here.
doc/Projects/2022/Project1/html/Project1-bs.html
+3 -3
View File
@@ -162,7 +162,7 @@ MathJax.Hub.Config({
</center>
<br>
<center>
<h4>Sep 5, 2022</h4>
<h4>Sep 6, 2022</h4>
</center> <!-- date -->
<br>
@@ -171,7 +171,7 @@ MathJax.Hub.Config({
<h2 id="regression-analysis-and-resampling-methods" class="anchor">Regression analysis and resampling methods </h2>
<p>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 <b>Franke</b> function
discussed here.
doc/Projects/2022/Project1/html/Project1.html
+3 -3
View File
@@ -201,13 +201,13 @@ MathJax.Hub.Config({
</center>
<br>
<center>
<h4>Sep 5, 2022</h4>
<h4>Sep 6, 2022</h4>
</center> <!-- date -->
<br>
<h2 id="regression-analysis-and-resampling-methods">Regression analysis and resampling methods </h2>
<p>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 <b>Franke</b> function
discussed here.
doc/Projects/2022/Project1/ipynb/Project1.ipynb
+53 -53
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@@ -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": {
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},
@@ -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
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@@ -85,7 +85,7 @@
},
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"id": "67acaf3f",
"metadata": {
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@@ -100,7 +100,7 @@
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{
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"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": {
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},
@@ -197,7 +197,7 @@
},
{
"cell_type": "markdown",
"id": "b8977dc1",
"id": "1a97a8a9",
"metadata": {
"editable": true
},
@@ -209,7 +209,7 @@
},
{
"cell_type": "markdown",
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"metadata": {
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@@ -220,7 +220,7 @@
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"metadata": {
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@@ -232,7 +232,7 @@
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"metadata": {
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@@ -244,7 +244,7 @@
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"metadata": {
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@@ -256,7 +256,7 @@
},
{
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"metadata": {
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@@ -267,7 +267,7 @@
},
{
"cell_type": "markdown",
"id": "d10e8fa5",
"id": "bc66b35b",
"metadata": {
"editable": true
},
@@ -279,7 +279,7 @@
},
{
"cell_type": "markdown",
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"id": "4eb37340",
"metadata": {
"editable": true
},
@@ -292,7 +292,7 @@
},
{
"cell_type": "markdown",
"id": "8635bca3",
"id": "325b1020",
"metadata": {
"editable": true
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@@ -304,7 +304,7 @@
},
{
"cell_type": "markdown",
"id": "da21e635",
"id": "df636f58",
"metadata": {
"editable": true
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@@ -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": {
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@@ -337,7 +337,7 @@
},
{
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"id": "1c015e08",
"id": "048427bb",
"metadata": {
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},
@@ -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": {
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},
@@ -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": {
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@@ -406,7 +406,7 @@
},
{
"cell_type": "markdown",
"id": "8d234f27",
"id": "36d8ba67",
"metadata": {
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@@ -418,7 +418,7 @@
},
{
"cell_type": "markdown",
"id": "3460f330",
"id": "277e8cbd",
"metadata": {
"editable": true
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@@ -447,7 +447,7 @@
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{
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"id": "e0056ae2",
"metadata": {
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@@ -479,7 +479,7 @@
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"id": "5bea6c04",
"metadata": {
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@@ -491,7 +491,7 @@
},
{
"cell_type": "markdown",
"id": "cc9118d1",
"id": "2b233d93",
"metadata": {
"editable": true
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@@ -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
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@@ -536,7 +536,7 @@
},
{
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"id": "e5ff09e8",
"id": "d42ac463",
"metadata": {
"editable": true
},
@@ -548,7 +548,7 @@
},
{
"cell_type": "markdown",
"id": "a39514b2",
"id": "93d7fb47",
"metadata": {
"editable": true
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@@ -558,7 +558,7 @@
},
{
"cell_type": "markdown",
"id": "4c34a548",
"id": "9e03ac19",
"metadata": {
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@@ -570,7 +570,7 @@
},
{
"cell_type": "markdown",
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"id": "c1a62cd8",
"metadata": {
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@@ -580,7 +580,7 @@
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"id": "2ceb482f",
"metadata": {
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@@ -592,7 +592,7 @@
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{
"cell_type": "markdown",
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"id": "61d409d6",
"metadata": {
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@@ -611,7 +611,7 @@
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{
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"id": "ef87f011",
"id": "d5b3b111",
"metadata": {
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@@ -636,7 +636,7 @@
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{
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"id": "19411bb1",
"metadata": {
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@@ -656,7 +656,7 @@
},
{
"cell_type": "markdown",
"id": "8ba58fed",
"id": "33c43b4e",
"metadata": {
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@@ -673,7 +673,7 @@
},
{
"cell_type": "markdown",
"id": "4b4db7cc",
"id": "1daf2ad3",
"metadata": {
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@@ -701,7 +701,7 @@
{
"cell_type": "code",
"execution_count": 2,
"id": "daf3d605",
"id": "26959ad8",
"metadata": {
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@@ -713,7 +713,7 @@
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@@ -725,7 +725,7 @@
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@@ -751,7 +751,7 @@
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@@ -776,7 +776,7 @@
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@@ -790,7 +790,7 @@
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@@ -820,7 +820,7 @@
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@@ -842,7 +842,7 @@
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doc/Projects/2022/Project1/ipynb/ipynb-Project1-src.tar.gz
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doc/Projects/2022/Project1/pdf/Project1.p.tex
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@@ -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.
doc/Projects/2022/Project1/pdf/Project1.pdf
BIN
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doc/Projects/2022/Project1/pdf/Project1.tex
+3 -3
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@@ -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.
doc/src/Projects/2022/Project1/Project1.do.txt
+2 -2
View File
@@ -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.