lars/FYS-STK4155
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

last update of typos?

Browse Source
This commit is contained in:
Morten Hjorth-Jensen
2022-09-05 17:21:37 +02:00
parent 3d4b9f7f4a
commit 09cc50ce2f
9 changed files with 51 additions and 65 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/Projects/2022/Project1/html/._Project1-bs000.html
-2
View File
@@ -491,8 +491,6 @@ of your model complexity (the degree of the polynomial) and the number
of data points, and possibly also your training and test data using the <b>bootstrap</b> resampling method.
You can follow the code example in the jupyter-book at <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#the-bias-variance-tradeoff" target="_self"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#the-bias-variance-tradeoff</tt></a>.
</p>
<p>Note also that when you calculate the bias, in all applications you don't know the function values \( f_i \). You would hence replace them with the actual data points \( y_i \).</p>
<h3 id="part-d-cross-validation-as-resampling-techniques-adding-more-complexity" class="anchor">Part d): Cross-validation as resampling techniques, adding more complexity </h3>
<p>The aim here is to write your own code for another widely popular
doc/Projects/2022/Project1/html/Project1-bs.html
-2
View File
@@ -491,8 +491,6 @@ of your model complexity (the degree of the polynomial) and the number
of data points, and possibly also your training and test data using the <b>bootstrap</b> resampling method.
You can follow the code example in the jupyter-book at <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#the-bias-variance-tradeoff" target="_self"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#the-bias-variance-tradeoff</tt></a>.
</p>
<p>Note also that when you calculate the bias, in all applications you don't know the function values \( f_i \). You would hence replace them with the actual data points \( y_i \).</p>
<h3 id="part-d-cross-validation-as-resampling-techniques-adding-more-complexity" class="anchor">Part d): Cross-validation as resampling techniques, adding more complexity </h3>
<p>The aim here is to write your own code for another widely popular
doc/Projects/2022/Project1/html/Project1.html
-2
View File
@@ -527,8 +527,6 @@ of your model complexity (the degree of the polynomial) and the number
of data points, and possibly also your training and test data using the <b>bootstrap</b> resampling method.
You can follow the code example in the jupyter-book at <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#the-bias-variance-tradeoff" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#the-bias-variance-tradeoff</tt></a>.
</p>
<p>Note also that when you calculate the bias, in all applications you don't know the function values \( f_i \). You would hence replace them with the actual data points \( y_i \).</p>
<h3 id="part-d-cross-validation-as-resampling-techniques-adding-more-complexity">Part d): Cross-validation as resampling techniques, adding more complexity </h3>
<p>The aim here is to write your own code for another widely popular
doc/Projects/2022/Project1/ipynb/Project1.ipynb
+51 -53
View File
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
"id": "2f0d0303",
"id": "ca1c2e24",
"metadata": {
"editable": true
},
@@ -14,7 +14,7 @@
},
{
"cell_type": "markdown",
"id": "383fa1e8",
"id": "02930a2d",
"metadata": {
"editable": true
},
@@ -27,7 +27,7 @@
},
{
"cell_type": "markdown",
"id": "00edcc37",
"id": "1333230f",
"metadata": {
"editable": true
},
@@ -63,7 +63,7 @@
},
{
"cell_type": "markdown",
"id": "b8f880fc",
"id": "3a7fd4e5",
"metadata": {
"editable": true
},
@@ -85,7 +85,7 @@
},
{
"cell_type": "markdown",
"id": "1e667978",
"id": "67295d49",
"metadata": {
"editable": true
},
@@ -100,7 +100,7 @@
},
{
"cell_type": "markdown",
"id": "3ce1ab3a",
"id": "13e004d0",
"metadata": {
"editable": true
},
@@ -129,7 +129,7 @@
{
"cell_type": "code",
"execution_count": 1,
"id": "965d1f48",
"id": "274827d8",
"metadata": {
"collapsed": false,
"editable": true
@@ -181,7 +181,7 @@
},
{
"cell_type": "markdown",
"id": "6171ab22",
"id": "95e656c7",
"metadata": {
"editable": true
},
@@ -197,7 +197,7 @@
},
{
"cell_type": "markdown",
"id": "969dd687",
"id": "719bdbb4",
"metadata": {
"editable": true
},
@@ -209,7 +209,7 @@
},
{
"cell_type": "markdown",
"id": "9194b1fc",
"id": "cd83cea7",
"metadata": {
"editable": true
},
@@ -220,7 +220,7 @@
},
{
"cell_type": "markdown",
"id": "cb6e0c5a",
"id": "3273d0b9",
"metadata": {
"editable": true
},
@@ -232,7 +232,7 @@
},
{
"cell_type": "markdown",
"id": "ef0c8ce0",
"id": "74550be5",
"metadata": {
"editable": true
},
@@ -244,7 +244,7 @@
},
{
"cell_type": "markdown",
"id": "c4e904d3",
"id": "fc4dbadf",
"metadata": {
"editable": true
},
@@ -256,7 +256,7 @@
},
{
"cell_type": "markdown",
"id": "8ce55ba5",
"id": "c6afdf2e",
"metadata": {
"editable": true
},
@@ -267,7 +267,7 @@
},
{
"cell_type": "markdown",
"id": "3aa94ac5",
"id": "5c4dc29b",
"metadata": {
"editable": true
},
@@ -279,7 +279,7 @@
},
{
"cell_type": "markdown",
"id": "8a15da0c",
"id": "acc5cbf5",
"metadata": {
"editable": true
},
@@ -292,7 +292,7 @@
},
{
"cell_type": "markdown",
"id": "9761270b",
"id": "d93a6eb5",
"metadata": {
"editable": true
},
@@ -304,7 +304,7 @@
},
{
"cell_type": "markdown",
"id": "12f47ebc",
"id": "152d18ff",
"metadata": {
"editable": true
},
@@ -314,7 +314,7 @@
},
{
"cell_type": "markdown",
"id": "be6e6c6e",
"id": "b850a49f",
"metadata": {
"editable": true
},
@@ -326,7 +326,7 @@
},
{
"cell_type": "markdown",
"id": "734989a3",
"id": "542f2716",
"metadata": {
"editable": true
},
@@ -337,7 +337,7 @@
},
{
"cell_type": "markdown",
"id": "7e01db05",
"id": "615e0737",
"metadata": {
"editable": true
},
@@ -359,7 +359,7 @@
},
{
"cell_type": "markdown",
"id": "6431c5d5",
"id": "786dc334",
"metadata": {
"editable": true
},
@@ -372,7 +372,7 @@
},
{
"cell_type": "markdown",
"id": "e4eae5de",
"id": "640d183d",
"metadata": {
"editable": true
},
@@ -384,7 +384,7 @@
},
{
"cell_type": "markdown",
"id": "6b7f7088",
"id": "4ea51524",
"metadata": {
"editable": true
},
@@ -396,7 +396,7 @@
},
{
"cell_type": "markdown",
"id": "57e680b6",
"id": "de75d17f",
"metadata": {
"editable": true
},
@@ -406,7 +406,7 @@
},
{
"cell_type": "markdown",
"id": "bbee460a",
"id": "da65f25e",
"metadata": {
"editable": true
},
@@ -418,7 +418,7 @@
},
{
"cell_type": "markdown",
"id": "ab772d43",
"id": "537854a8",
"metadata": {
"editable": true
},
@@ -447,7 +447,7 @@
},
{
"cell_type": "markdown",
"id": "022b31d9",
"id": "8272e5b5",
"metadata": {
"editable": true
},
@@ -479,7 +479,7 @@
},
{
"cell_type": "markdown",
"id": "38c61204",
"id": "dc84027a",
"metadata": {
"editable": true
},
@@ -491,7 +491,7 @@
},
{
"cell_type": "markdown",
"id": "aa00d7dd",
"id": "a3e762aa",
"metadata": {
"editable": true
},
@@ -510,7 +510,7 @@
},
{
"cell_type": "markdown",
"id": "6240984f",
"id": "17aab5fa",
"metadata": {
"editable": true
},
@@ -522,7 +522,7 @@
},
{
"cell_type": "markdown",
"id": "0f01484d",
"id": "ff787790",
"metadata": {
"editable": true
},
@@ -536,7 +536,7 @@
},
{
"cell_type": "markdown",
"id": "37f1b3f8",
"id": "094a8507",
"metadata": {
"editable": true
},
@@ -548,7 +548,7 @@
},
{
"cell_type": "markdown",
"id": "0673dacf",
"id": "44017f87",
"metadata": {
"editable": true
},
@@ -558,7 +558,7 @@
},
{
"cell_type": "markdown",
"id": "281ae919",
"id": "0e4bc241",
"metadata": {
"editable": true
},
@@ -570,7 +570,7 @@
},
{
"cell_type": "markdown",
"id": "3604e435",
"id": "226339d0",
"metadata": {
"editable": true
},
@@ -580,7 +580,7 @@
},
{
"cell_type": "markdown",
"id": "cb6e58c2",
"id": "22627a64",
"metadata": {
"editable": true
},
@@ -592,7 +592,7 @@
},
{
"cell_type": "markdown",
"id": "65111a57",
"id": "e692e756",
"metadata": {
"editable": true
},
@@ -606,14 +606,12 @@
"Discuss the bias and variance trade-off as function\n",
"of your model complexity (the degree of the polynomial) and the number\n",
"of data points, and possibly also your training and test data using the **bootstrap** resampling method.\n",
"You can follow the code example in the jupyter-book at <https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#the-bias-variance-tradeoff>.\n",
"\n",
"Note also that when you calculate the bias, in all applications you don't know the function values $f_i$. You would hence replace them with the actual data points $y_i$."
"You can follow the code example in the jupyter-book at <https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#the-bias-variance-tradeoff>."
]
},
{
"cell_type": "markdown",
"id": "54283cc7",
"id": "8dc1e0c1",
"metadata": {
"editable": true
},
@@ -638,7 +636,7 @@
},
{
"cell_type": "markdown",
"id": "47078a1a",
"id": "b7e92ecb",
"metadata": {
"editable": true
},
@@ -658,7 +656,7 @@
},
{
"cell_type": "markdown",
"id": "97849c2c",
"id": "d5c0bb8b",
"metadata": {
"editable": true
},
@@ -675,7 +673,7 @@
},
{
"cell_type": "markdown",
"id": "c37b1811",
"id": "f253b365",
"metadata": {
"editable": true
},
@@ -703,7 +701,7 @@
{
"cell_type": "code",
"execution_count": 2,
"id": "646b70df",
"id": "bbd345fb",
"metadata": {
"collapsed": false,
"editable": true
@@ -715,7 +713,7 @@
},
{
"cell_type": "markdown",
"id": "5107f3b6",
"id": "8342b19a",
"metadata": {
"editable": true
},
@@ -727,7 +725,7 @@
{
"cell_type": "code",
"execution_count": 3,
"id": "24ae3ba0",
"id": "adf76147",
"metadata": {
"collapsed": false,
"editable": true
@@ -753,7 +751,7 @@
},
{
"cell_type": "markdown",
"id": "1f239875",
"id": "e7ed037b",
"metadata": {
"editable": true
},
@@ -778,7 +776,7 @@
},
{
"cell_type": "markdown",
"id": "ea729b77",
"id": "5a0572b6",
"metadata": {
"editable": true
},
@@ -792,7 +790,7 @@
},
{
"cell_type": "markdown",
"id": "61fd5713",
"id": "ac2218c8",
"metadata": {
"editable": true
},
@@ -822,7 +820,7 @@
},
{
"cell_type": "markdown",
"id": "9579c5f3",
"id": "32926fbf",
"metadata": {
"editable": true
},
@@ -844,7 +842,7 @@
},
{
"cell_type": "markdown",
"id": "ecf2eb04",
"id": "c16d8495",
"metadata": {
"editable": true
},
doc/Projects/2022/Project1/ipynb/ipynb-Project1-src.tar.gz
BIN
View File
Binary file not shown.
doc/Projects/2022/Project1/pdf/Project1.p.tex
-2
View File
@@ -450,8 +450,6 @@ of your model complexity (the degree of the polynomial) and the number
of data points, and possibly also your training and test data using the \textbf{bootstrap} resampling method.
You can follow the code example in the jupyter-book at \href{{https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#the-bias-variance-tradeoff}}{\nolinkurl{https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html\#the-bias-variance-tradeoff}}.
Note also that when you calculate the bias, in all applications you don't know the function values $f_i$. You would hence replace them with the actual data points $y_i$.
\paragraph{Part d): Cross-validation as resampling techniques, adding more complexity.}
The aim here is to write your own code for another widely popular
resampling technique, the so-called cross-validation method. Again,
doc/Projects/2022/Project1/pdf/Project1.pdf
BIN
View File
Binary file not shown.
doc/Projects/2022/Project1/pdf/Project1.tex
-2
View File
@@ -420,8 +420,6 @@ of your model complexity (the degree of the polynomial) and the number
of data points, and possibly also your training and test data using the \textbf{bootstrap} resampling method.
You can follow the code example in the jupyter-book at \href{{https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#the-bias-variance-tradeoff}}{\nolinkurl{https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html\#the-bias-variance-tradeoff}}.
Note also that when you calculate the bias, in all applications you don't know the function values $f_i$. You would hence replace them with the actual data points $y_i$.
\paragraph{Part d): Cross-validation as resampling techniques, adding more complexity.}
The aim here is to write your own code for another widely popular
resampling technique, the so-called cross-validation method. Again,
doc/src/Projects/2022/Project1/Project1.do.txt
-2
View File
@@ -320,8 +320,6 @@ of your model complexity (the degree of the polynomial) and the number
of data points, and possibly also your training and test data using the _bootstrap_ resampling method.
You can follow the code example in the jupyter-book at URL:"https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#the-bias-variance-tradeoff".
Note also that when you calculate the bias, in all applications you don't know the function values $f_i$. You would hence replace them with the actual data points $y_i$.
=== Part d): Cross-validation as resampling techniques, adding more complexity ===