typo

Author: mhjensen

doc/LectureNotes/_build/.doctrees/environment.pickle

@@ -39,7 +39,7 @@
     "links at\n",
     "<https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects>\n",
     "contain more information. There you can find examples of previous\n",
-    "reports, the projects themselves, how we rade reports etc. How to\n",
+    "reports, the projects themselves, how we grade reports etc. How to\n",
     "write reports will also be discussed during the various lab\n",
     "sessions. Please do ask us if you are in doubt.\n",
     "\n",
@@ -532,7 +532,7 @@
     "\n",
     "1. For a discussion and derivation of the variances and mean squared errors using linear regression, see the [Lecture notes on ridge regression by Wessel N. van Wieringen](https://arxiv.org/abs/1509.09169)\n",
     "\n",
-    "2. The textbook of [Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer](https://www.springer.com/gp/book/9780387848570), chapters 3 and 7 are the most relevant ones for the analysis here."
+    "2. The textbook of [Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer](https://www.springer.com/gp/book/9780387848570), chapters 3 and 7 are the most relevant ones for the analysis of parts g) and h)."
    ]
   },
   {
@@ -635,7 +635,25 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }

doc/LectureNotes/_build/.doctrees/project1.doctree

@@ -421,7 +421,7 @@ <h2>Preamble: Note on writing reports, using reference material, AI and other to
 links at
 <a class="github reference external" href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects">CompPhysics/MachineLearning</a>
 contain more information. There you can find examples of previous
-reports, the projects themselves, how we rade reports etc. How to
+reports, the projects themselves, how we grade reports etc. How to
 write reports will also be discussed during the various lab
 sessions. Please do ask us if you are in doubt.</p>
 <p>When using codes and material from other sources, you should refer to
@@ -651,7 +651,7 @@ <h3>Part h):  Cross-validation as resampling techniques, adding more complexity<
 <h2>Background literature<a class="headerlink" href="#background-literature" title="Link to this heading">#</a></h2>
 <ol class="arabic simple">
 <li><p>For a discussion and derivation of the variances and mean squared errors using linear regression, see the <a class="reference external" href="https://arxiv.org/abs/1509.09169">Lecture notes on ridge regression by Wessel N. van Wieringen</a></p></li>
-<li><p>The textbook of <a class="reference external" href="https://www.springer.com/gp/book/9780387848570">Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer</a>, chapters 3 and 7 are the most relevant ones for the analysis here.</p></li>
+<li><p>The textbook of <a class="reference external" href="https://www.springer.com/gp/book/9780387848570">Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer</a>, chapters 3 and 7 are the most relevant ones for the analysis of parts g) and h).</p></li>
 </ol>
 </section>
 <section id="introduction-to-numerical-projects">

doc/LectureNotes/_build/html/_sources/project1.ipynb

@@ -39,7 +39,7 @@
     "links at\n",
     "<https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects>\n",
     "contain more information. There you can find examples of previous\n",
-    "reports, the projects themselves, how we rade reports etc. How to\n",
+    "reports, the projects themselves, how we grade reports etc. How to\n",
     "write reports will also be discussed during the various lab\n",
     "sessions. Please do ask us if you are in doubt.\n",
     "\n",
@@ -532,7 +532,7 @@
     "\n",
     "1. For a discussion and derivation of the variances and mean squared errors using linear regression, see the [Lecture notes on ridge regression by Wessel N. van Wieringen](https://arxiv.org/abs/1509.09169)\n",
     "\n",
-    "2. The textbook of [Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer](https://www.springer.com/gp/book/9780387848570), chapters 3 and 7 are the most relevant ones for the analysis here."
+    "2. The textbook of [Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer](https://www.springer.com/gp/book/9780387848570), chapters 3 and 7 are the most relevant ones for the analysis of parts g) and h)."
    ]
   },
   {
@@ -635,7 +635,25 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }

doc/LectureNotes/_build/html/project1.html

@@ -39,7 +39,7 @@
     "links at\n",
     "<https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects>\n",
     "contain more information. There you can find examples of previous\n",
-    "reports, the projects themselves, how we rade reports etc. How to\n",
+    "reports, the projects themselves, how we grade reports etc. How to\n",
     "write reports will also be discussed during the various lab\n",
     "sessions. Please do ask us if you are in doubt.\n",
     "\n",
@@ -532,7 +532,7 @@
     "\n",
     "1. For a discussion and derivation of the variances and mean squared errors using linear regression, see the [Lecture notes on ridge regression by Wessel N. van Wieringen](https://arxiv.org/abs/1509.09169)\n",
     "\n",
-    "2. The textbook of [Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer](https://www.springer.com/gp/book/9780387848570), chapters 3 and 7 are the most relevant ones for the analysis here."
+    "2. The textbook of [Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer](https://www.springer.com/gp/book/9780387848570), chapters 3 and 7 are the most relevant ones for the analysis of parts g) and h)."
    ]
   },
   {
@@ -635,7 +635,25 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }