From 4a3985783fc7575de287f702606be5e1b6cec185 Mon Sep 17 00:00:00 2001
From: Morten Hjorth-Jensen <morten.hjorth-jensen@fys.uio.no>
Date: Tue, 10 Sep 2024 10:44:48 +0200
Subject: [PATCH] updated lecture notes with video of exercises

---
 .../_build/.doctrees/environment.pickle       | Bin 309149 -> 309165 bytes
 .../_build/.doctrees/week37.doctree           | Bin 590937 -> 590881 bytes
 .../_build/html/_images/week37_121_0.png      | Bin 22325 -> 21911 bytes
 .../_build/html/_sources/week37.ipynb         | 354 +++++++--------
 doc/LectureNotes/_build/html/searchindex.js   |   2 +-
 doc/LectureNotes/_build/html/week37.html      |  30 +-
 .../_build/jupyter_execute/week37.ipynb       | 403 +++++++++---------
 .../_build/jupyter_execute/week37.py          |   2 +
 .../_build/jupyter_execute/week37_121_0.png   | Bin 22325 -> 21911 bytes
 doc/LectureNotes/week37.ipynb                 | 354 +++++++--------
 10 files changed, 582 insertions(+), 563 deletions(-)

diff --git a/doc/LectureNotes/_build/.doctrees/environment.pickle b/doc/LectureNotes/_build/.doctrees/environment.pickle
index fda84d9764f7054ff1828d94cfadc14713dd22f0..14e834fcf13e5055addfc76a9e19e86fbb9f7d2b 100644
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diff --git a/doc/LectureNotes/_build/html/_sources/week37.ipynb b/doc/LectureNotes/_build/html/_sources/week37.ipynb
index 70763a9b5..c611a8dbf 100644
--- a/doc/LectureNotes/_build/html/_sources/week37.ipynb
+++ b/doc/LectureNotes/_build/html/_sources/week37.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "53365fbb",
+   "id": "7ce0c2c8",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "809960d9",
+   "id": "3bd9c0ba",
    "metadata": {
     "editable": true
    },
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0cac33e9",
+   "id": "be6135df",
    "metadata": {
     "editable": true
    },
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d5ba8d48",
+   "id": "3ac97fd3",
    "metadata": {
     "editable": true
    },
@@ -77,12 +77,14 @@
     "\n",
     "  * Work on project 1\n",
     "\n",
+    "  * [Video of exercise sessions week 37](https://youtu.be/bK4AEcTu-oM)\n",
+    "\n",
     "  * For more discussions of Ridge regression and calculation of averages, [Wessel van Wieringen's](https://arxiv.org/abs/1509.09169) article is highly recommended."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "560073ae",
+   "id": "7010206d",
    "metadata": {
     "editable": true
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@@ -92,7 +94,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "58ff8482",
+   "id": "2467ccb1",
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     "editable": true
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@@ -115,7 +117,7 @@
   },
   {
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-   "id": "c9ed29f5",
+   "id": "4e2b9f77",
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@@ -127,7 +129,7 @@
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   {
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-   "id": "f3e177df",
+   "id": "aa3e18f2",
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@@ -140,7 +142,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "943d3101",
+   "id": "a9836e09",
    "metadata": {
     "editable": true
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@@ -152,7 +154,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bddef09d",
+   "id": "81c40e6c",
    "metadata": {
     "editable": true
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@@ -164,7 +166,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8d119a52",
+   "id": "fd6babf0",
    "metadata": {
     "editable": true
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@@ -176,7 +178,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b67e829d",
+   "id": "71dfe09f",
    "metadata": {
     "editable": true
    },
@@ -187,7 +189,7 @@
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   {
    "cell_type": "markdown",
-   "id": "3a76ed1c",
+   "id": "90f49395",
    "metadata": {
     "editable": true
    },
@@ -199,7 +201,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "58863d67",
+   "id": "eb1d8923",
    "metadata": {
     "editable": true
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@@ -210,7 +212,7 @@
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   {
    "cell_type": "markdown",
-   "id": "f3c19d2c",
+   "id": "a3b37065",
    "metadata": {
     "editable": true
    },
@@ -222,7 +224,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fe7606c4",
+   "id": "092a8fe2",
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     "editable": true
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@@ -232,7 +234,7 @@
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   {
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-   "id": "e1d5e31c",
+   "id": "f15ab83a",
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@@ -263,7 +265,7 @@
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+   "id": "5ed97aa3",
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@@ -275,7 +277,7 @@
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-   "id": "46e9c4fd",
+   "id": "7b570c43",
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@@ -287,7 +289,7 @@
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-   "id": "20ca4ccb",
+   "id": "8a82d943",
    "metadata": {
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@@ -297,7 +299,7 @@
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   {
    "cell_type": "markdown",
-   "id": "651adbb5",
+   "id": "a9fe176c",
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@@ -309,7 +311,7 @@
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   {
    "cell_type": "markdown",
-   "id": "a0d30507",
+   "id": "720c650c",
    "metadata": {
     "editable": true
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@@ -319,7 +321,7 @@
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   {
    "cell_type": "markdown",
-   "id": "f3269fe5",
+   "id": "4c13afe3",
    "metadata": {
     "editable": true
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@@ -331,7 +333,7 @@
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-   "id": "f7096467",
+   "id": "f23aa0de",
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@@ -341,7 +343,7 @@
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-   "id": "e35c1080",
+   "id": "cc0ec7c0",
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@@ -353,7 +355,7 @@
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-   "id": "6392c0ea",
+   "id": "71275e73",
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@@ -363,7 +365,7 @@
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-   "id": "ac50371f",
+   "id": "4c41ecf0",
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@@ -383,7 +385,7 @@
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+   "id": "890ba6f9",
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@@ -395,7 +397,7 @@
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-   "id": "ab4c6470",
+   "id": "dfcd3bf6",
    "metadata": {
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@@ -405,7 +407,7 @@
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   {
    "cell_type": "markdown",
-   "id": "08a17057",
+   "id": "956837e7",
    "metadata": {
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@@ -417,7 +419,7 @@
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@@ -429,7 +431,7 @@
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@@ -441,7 +443,7 @@
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@@ -453,7 +455,7 @@
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@@ -465,7 +467,7 @@
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@@ -477,7 +479,7 @@
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@@ -489,7 +491,7 @@
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@@ -501,7 +503,7 @@
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@@ -511,7 +513,7 @@
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@@ -523,7 +525,7 @@
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@@ -533,7 +535,7 @@
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@@ -552,7 +554,7 @@
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@@ -573,7 +575,7 @@
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@@ -585,7 +587,7 @@
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@@ -596,7 +598,7 @@
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@@ -609,7 +611,7 @@
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@@ -621,7 +623,7 @@
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@@ -631,7 +633,7 @@
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@@ -643,7 +645,7 @@
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@@ -653,7 +655,7 @@
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@@ -665,7 +667,7 @@
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@@ -675,7 +677,7 @@
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@@ -689,7 +691,7 @@
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@@ -701,7 +703,7 @@
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@@ -711,7 +713,7 @@
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@@ -723,7 +725,7 @@
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@@ -733,7 +735,7 @@
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@@ -745,7 +747,7 @@
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@@ -755,7 +757,7 @@
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@@ -767,7 +769,7 @@
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@@ -777,7 +779,7 @@
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@@ -793,7 +795,7 @@
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@@ -805,7 +807,7 @@
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@@ -815,7 +817,7 @@
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@@ -827,7 +829,7 @@
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@@ -840,7 +842,7 @@
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@@ -852,7 +854,7 @@
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@@ -862,7 +864,7 @@
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@@ -874,7 +876,7 @@
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@@ -884,7 +886,7 @@
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@@ -896,7 +898,7 @@
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@@ -908,7 +910,7 @@
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@@ -918,7 +920,7 @@
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@@ -930,7 +932,7 @@
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@@ -942,7 +944,7 @@
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@@ -954,7 +956,7 @@
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@@ -964,7 +966,7 @@
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@@ -976,7 +978,7 @@
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@@ -986,7 +988,7 @@
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@@ -1005,7 +1007,7 @@
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@@ -1033,7 +1035,7 @@
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@@ -1059,7 +1061,7 @@
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@@ -1076,7 +1078,7 @@
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@@ -1096,7 +1098,7 @@
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@@ -1125,7 +1127,7 @@
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@@ -1150,7 +1152,7 @@
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@@ -1170,7 +1172,7 @@
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@@ -1182,7 +1184,7 @@
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@@ -1192,7 +1194,7 @@
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@@ -1207,7 +1209,7 @@
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@@ -1220,7 +1222,7 @@
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@@ -1233,7 +1235,7 @@
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@@ -1245,7 +1247,7 @@
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@@ -1258,7 +1260,7 @@
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@@ -1269,7 +1271,7 @@
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@@ -1283,7 +1285,7 @@
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@@ -1293,7 +1295,7 @@
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@@ -1307,7 +1309,7 @@
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@@ -1320,7 +1322,7 @@
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@@ -1333,7 +1335,7 @@
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@@ -1343,7 +1345,7 @@
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@@ -1356,7 +1358,7 @@
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@@ -1366,7 +1368,7 @@
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@@ -1379,7 +1381,7 @@
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@@ -1391,7 +1393,7 @@
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@@ -1410,7 +1412,7 @@
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@@ -1423,7 +1425,7 @@
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@@ -1435,7 +1437,7 @@
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@@ -1448,7 +1450,7 @@
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@@ -1468,7 +1470,7 @@
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@@ -1491,7 +1493,7 @@
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@@ -1506,7 +1508,7 @@
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@@ -1518,7 +1520,7 @@
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@@ -1538,7 +1540,7 @@
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@@ -1558,7 +1560,7 @@
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@@ -1582,7 +1584,7 @@
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@@ -1603,7 +1605,7 @@
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@@ -1633,7 +1635,7 @@
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@@ -1657,7 +1659,7 @@
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@@ -1696,7 +1698,7 @@
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@@ -1706,7 +1708,7 @@
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@@ -1717,7 +1719,7 @@
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@@ -1737,7 +1739,7 @@
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@@ -1755,7 +1757,7 @@
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@@ -1767,7 +1769,7 @@
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@@ -1784,7 +1786,7 @@
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@@ -1796,7 +1798,7 @@
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@@ -1806,7 +1808,7 @@
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@@ -1818,7 +1820,7 @@
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@@ -1835,7 +1837,7 @@
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@@ -1847,7 +1849,7 @@
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@@ -1857,7 +1859,7 @@
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@@ -1869,7 +1871,7 @@
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class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning","<span class=\"section-number\">16. </span>Convolutional Neural Networks","<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view","<span class=\"section-number\">4. </span>Ridge and Lasso Regression","<span class=\"section-number\">5. </span>Resampling Methods","<span class=\"section-number\">6. </span>Logistic Regression","<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims","<span class=\"section-number\">9. </span>Decision trees, overarching aims","<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)","<span class=\"section-number\">13. </span>Neural networks","<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm","<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Applied Data Analysis and Machine Learning","<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 7 (midnight), 2024","Teaching schedule with links to material","<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Linear Regression and Statistical interpretations","Week 37: Statistical interpretations and Resampling 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class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning","<span class=\"section-number\">16. </span>Convolutional Neural Networks","<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view","<span class=\"section-number\">4. </span>Ridge and Lasso Regression","<span class=\"section-number\">5. </span>Resampling Methods","<span class=\"section-number\">6. </span>Logistic Regression","<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims","<span class=\"section-number\">9. </span>Decision trees, overarching aims","<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)","<span class=\"section-number\">13. </span>Neural networks","<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm","<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Applied Data Analysis and Machine Learning","<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 7 (midnight), 2024","Teaching schedule with links to material","<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Linear Regression and Statistical interpretations","Week 37: Statistical interpretations and Resampling Methods"],titleterms:{"1":[0,15,16,17,18,22,27,28],"14":[],"17":[],"18":[],"2":[0,15,16,17,18,27,28,29],"2023":25,"2024":22,"26":28,"3":[0,15,16,27,28],"34":[15,27],"35":[16,28],"36":[17,29],"37":[18,30],"38":19,"4":[0,28],"5":0,"7":22,"9":30,"case":[8,10,24,28,29],"do":[1,29,30],"final":[12,28,29],"function":[0,1,6,7,8,10,11,12,13,22,24,27,28,29,30],"import":[5,21,27,28,29],"new":[4,29,30],A:[0,1,4,8,9,27,29,30],And:[27,28,29],For:28,In:25,Ising:6,The:[0,1,2,3,5,6,7,8,9,11,12,17,20,27,28,29,30],To:[27,28],With:[4,29],about:[27,28],abov:29,activ:[1,12,29],ad:[0,6,17,22,27,28],adaboost:10,adagrad:13,adam:13,adapt:10,adjust:1,adversari:4,again:[3,9],ai:[22,27],aim:[8,9,17,18,19,27],aka:[27,28],algebra:[21,27],algorithm:[9,10,11,12,28],algortithm:13,all:8,altern:[],an:[0,4,10,27],analys:[5,28],analysi:[0,5,6,11,20,22,24,27,28,29,30],analyt:[0,16,17],analyz:[],ani:13,anoth:[9,29,30],appli:20,approach:[0,8,14,27,30],approxim:12,architectur:1,arrai:[21,27],assist:25,assumpt:[29,30],august:28,autocorrel:24,autograd:[2,13],automat:13,b:[17,22],back:[1,11,12],background:[20,22,30],bag:10,base:[13,30],basic:[0,5,7,9,10,11,21,28,29,30],batch:1,bay:[5,29,30],befor:11,beta:[29,30],better:8,bia:[6,22,30],binari:1,bind:27,bird:10,boldsymbol:[28,29,30],boost:10,bootstrap:[6,10,30],boston:0,breast:1,brief:[27,30],bring:12,build:[1,3,9],c:[22,27],calcul:28,can:[27,30],cancer:[1,7,9,11],cart:9,center:28,central:[13,20,24,30],chain:12,chang:10,channel:27,chi:[0,27],choos:1,cifar01:3,classic:11,classif:[1,9,10],classifi:8,clip:1,cluster:14,cnn:3,code:[0,1,2,5,9,11,12,13,14,27,28,29,30],collect:[1,3],commun:27,compar:[2,10],comparison:29,complet:28,complex:[0,6,22,28],complic:6,compon:11,comput:9,computation:30,computerlab:27,con:9,concept:24,condit:[29,30],confid:30,conjug:13,contn:27,convex:[8,13],convolut:[3,12],correctli:[29,30],correl:[11,28],cost:[1,10,28,29,30],cours:[20,26,27],covari:[5,11,24,28],cover:27,critic:[],cross:[6,22,30],cython:27,d:22,data:[0,1,3,6,7,9,11,15,16,20,22,24,27,28,29],dataset:[1,3],deadlin:[22,27],decai:2,decemb:[],decis:[9,10],decomposit:[5,11,17,21,28],deep:[1,2,27],defin:[1,27],degre:[0,28],deliveri:22,delta:30,dens:[0,27],deriv:[5,12,28,29,30],descent:[2,10,13],descript:22,design:28,detail:[3,27],develop:1,diagon:11,differ:8,differenti:[2,13],diffus:2,dimension:[2,3,8,22,28],disadvantag:9,discret:24,distribut:[5,24,29,30],distrubut:30,doe:[28,29],domain:24,down:1,dropout:1,e:22,economi:28,electron:22,element:[0,24,27],elimin:21,energi:27,ensembl:10,entropi:9,environ:[0,15,27],equat:[0,2,12,27,28,29],error:[0,10,27,28,30],essenti:27,estim:[29,30],etc:27,euler:2,evalu:1,exampl:[0,1,2,3,4,6,7,8,9,10,27,28,29,30],exercis:[0,6,15,16,17,18,19,27,28],expect:[18,24,29,30],expens:30,experi:24,explor:[0,15,16,27],exponenti:2,express:[17,18,28],extrapol:4,extrem:[10,27],ey:10,f:22,fall:25,famili:[1,27,28],famou:21,fantast:28,featur:[9,21,28],feed:[1,12],find:30,fine:1,first:[4,12,27,28,29],fit:[0,10,27,29],fix:28,fold:30,forc:3,forest:10,format:[22,27],forward:[1,2,12],fourier:3,frank:[6,22,28],freedom:[0,28],frequent:28,frequentist:[0,27],from:[5,10,12,28,29,30],full:2,further:[3,5,28],g:22,gan:4,gaussian:21,gd:13,gener:[4,9,27],geometr:11,gini:9,good:[0,27],grade:[25,27],gradient:[1,2,10,13],growth:2,ha:20,handl:[21,27,28],happen:[29,30],hessian:28,hidden:2,histogram:30,hous:0,hyperparamet:1,hyperplan:8,i:1,id3:9,idea:11,ident:[29,30],identifi:30,ii:27,iid:[29,30],illustr:29,implement:1,implic:[5,28],improv:1,includ:13,increment:11,independ:[29,30],index:9,inform:25,input:2,instal:[20,22,27],instructor:25,intercept:28,interpret:[5,11,27,28,29,30],interv:30,introduc:[11,13,28],introduct:[0,6,20,21,22,27],invers:[5,21,29],invert:28,iter:10,its:28,jacobian:28,jax:13,julia:27,jungl:10,k:30,kera:[1,3],kernel:[8,11],lab:30,lagrangian:8,lasso:[5,6,22,28,29,30],last:28,later:[5,28],layer:[1,2,3,12],learn:[0,1,2,11,13,14,15,16,20,22,27,28,29,30],least:[5,6,18,22,27,28,29],lectur:[27,28,29,30],level:10,librari:[20,27],likelihood:[7,29,30],limit:[1,13,24,30],linear:[0,8,13,21,27,28,29],link:[5,11,23,26,28,29,30],literatur:22,logist:[7,27],loss:28,lu:21,machin:[0,8,13,20,22,27],made:[29,30],main:[24,27],make:[0,9,10,15,16,27,28],mani:[10,12],manipul:28,margin:[29,30],mass:27,materi:[22,23,27,28,29,30],math:[5,28],mathemat:[3,5,8,28],matric:[5,21,27,29],matrix:[1,5,11,12,21,27,28,29],matter:[0,27],max:28,maximum:[29,30],mean:[0,28,29],meet:[5,10,24,27,28],mercer:8,method:[6,9,10,13,22,27,30],midnight:22,min:28,minim:27,ml:27,mle:[29,30],mlp:12,mnist:[3,4],model:[0,1,4,6,12,27],momentum:13,mondai:[28,29,30],moon:[8,9],more:[3,6,21,22,27,28,29,30],multilay:12,multipl:[1,3],multipli:8,need:[22,27],network:[1,2,3,4,7,12,27],neural:[1,2,3,4,7,12,27],non:8,normal:[0,1,30],notat:12,note:[22,28,29],novemb:[],now:[1,9,13,29,30],nuclear:[0,27],numba:27,number:[0,2,24,28],numer:[2,22,24],numpi:[21,27],object:3,obtain:11,octob:22,od:2,off:[6,22],ol:[5,6,22,29,30],one:[2,12],oper:21,optim:[1,8,13,20,27,28],order:13,ordinari:[5,6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\ No newline at end of file
diff --git a/doc/LectureNotes/_build/html/week37.html b/doc/LectureNotes/_build/html/week37.html
index 754b10159..b00d26a28 100644
--- a/doc/LectureNotes/_build/html/week37.html
+++ b/doc/LectureNotes/_build/html/week37.html
@@ -990,6 +990,7 @@ doconce format html week37.do.txt --no_mako -->
 <li><p>Discussion of resampling techniques</p></li>
 <li><p>Exercise set for week 37</p></li>
 <li><p>Work on project 1</p></li>
+<li><p><a class="reference external" href="https://youtu.be/bK4AEcTu-oM">Video of exercise sessions week 37</a></p></li>
 <li><p>For more discussions of Ridge regression and calculation of averages, <a class="reference external" href="https://arxiv.org/abs/1509.09169">Wessel van Wieringen’s</a> article is highly recommended.</p></li>
 </ul>
 </div>
@@ -1623,7 +1624,7 @@ theorem.</p>
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Bootstrap Statistics :
 original           bias      std. error
- 100.135  15.1329        100.136        0.151103
+ 99.7217  14.8193        99.7194        0.147557
 </pre></div>
 </div>
 </div>
@@ -1848,7 +1849,9 @@ Error: 0.10398646080125035
 Bias^2: 0.1007711427354898
 Var: 0.0032153180657605116
 0.10398646080125035 &gt;= 0.1007711427354898 + 0.0032153180657605116 = 0.10398646080125032
-Polynomial degree: 3
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 3
 Error: 0.06547790180152355
 Bias^2: 0.06208238634231949
 Var: 0.0033955154592040936
@@ -1880,14 +1883,17 @@ Error: 0.017355848195593347
 Bias^2: 0.010331721306655127
 Var: 0.007024126888938232
 0.017355848195593347 &gt;= 0.010331721306655127 + 0.007024126888938232 = 0.01735584819559336
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 9
+Polynomial degree: 9
 Error: 0.02660572763718093
 Bias^2: 0.010018312644137363
 Var: 0.016587414993043573
 0.02660572763718093 &gt;= 0.010018312644137363 + 0.016587414993043573 = 0.026605727637180936
-Polynomial degree: 10
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree:
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 10
 Error: 0.021592704588025025
 Bias^2: 0.010516485576645508
 Var: 0.011076219011379514
@@ -1897,9 +1903,7 @@ Error: 0.07160048164233104
 Bias^2: 0.014436800088904942
 Var: 0.05716368155342608
 0.07160048164233104 &gt;= 0.014436800088904942 + 0.05716368155342608 = 0.07160048164233102
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 12
+Polynomial degree: 12
 Error: 0.11547777218872497
 Bias^2: 0.01628578269596628
 Var: 0.09919198949275869
@@ -1911,7 +1915,7 @@ Var: 0.20867052175034223
 0.22842468702219465 &gt;= 0.01975416527185249 + 0.20867052175034223 = 0.2284246870221947
 </pre></div>
 </div>
-<img alt="_images/week37_139_4.png" src="_images/week37_139_4.png" />
+<img alt="_images/week37_139_5.png" src="_images/week37_139_5.png" />
 </div>
 </div>
 </div>
@@ -2342,9 +2346,9 @@ Mean squared error on training data: 0.00063866
 Mean squared error on test data: 3099.60342978
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_96719/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_405/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(trainingerror), label=&#39;Training Error&#39;)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_96719/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_405/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(testerror), label=&#39;Test Error&#39;)
 </pre></div>
 </div>
@@ -2429,7 +2433,7 @@ Mean squared error on test data: 3099.60342978
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_96719/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_405/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(estimated_mse_sklearn), label=&#39;Test Error&#39;)
 </pre></div>
 </div>
diff --git a/doc/LectureNotes/_build/jupyter_execute/week37.ipynb b/doc/LectureNotes/_build/jupyter_execute/week37.ipynb
index 2ada663bf..5545aeebf 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week37.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week37.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "53365fbb",
+   "id": "7ce0c2c8",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "809960d9",
+   "id": "3bd9c0ba",
    "metadata": {
     "editable": true
    },
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0cac33e9",
+   "id": "be6135df",
    "metadata": {
     "editable": true
    },
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d5ba8d48",
+   "id": "3ac97fd3",
    "metadata": {
     "editable": true
    },
@@ -77,12 +77,14 @@
     "\n",
     "  * Work on project 1\n",
     "\n",
+    "  * [Video of exercise sessions week 37](https://youtu.be/bK4AEcTu-oM)\n",
+    "\n",
     "  * For more discussions of Ridge regression and calculation of averages, [Wessel van Wieringen's](https://arxiv.org/abs/1509.09169) article is highly recommended."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "560073ae",
+   "id": "7010206d",
    "metadata": {
     "editable": true
    },
@@ -92,7 +94,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "58ff8482",
+   "id": "2467ccb1",
    "metadata": {
     "editable": true
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@@ -115,7 +117,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c9ed29f5",
+   "id": "4e2b9f77",
    "metadata": {
     "editable": true
    },
@@ -127,7 +129,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f3e177df",
+   "id": "aa3e18f2",
    "metadata": {
     "editable": true
    },
@@ -140,7 +142,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "943d3101",
+   "id": "a9836e09",
    "metadata": {
     "editable": true
    },
@@ -152,7 +154,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bddef09d",
+   "id": "81c40e6c",
    "metadata": {
     "editable": true
    },
@@ -164,7 +166,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8d119a52",
+   "id": "fd6babf0",
    "metadata": {
     "editable": true
    },
@@ -176,7 +178,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b67e829d",
+   "id": "71dfe09f",
    "metadata": {
     "editable": true
    },
@@ -187,7 +189,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3a76ed1c",
+   "id": "90f49395",
    "metadata": {
     "editable": true
    },
@@ -199,7 +201,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "58863d67",
+   "id": "eb1d8923",
    "metadata": {
     "editable": true
    },
@@ -210,7 +212,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f3c19d2c",
+   "id": "a3b37065",
    "metadata": {
     "editable": true
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@@ -222,7 +224,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fe7606c4",
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@@ -232,7 +234,7 @@
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@@ -263,7 +265,7 @@
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@@ -275,7 +277,7 @@
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@@ -287,7 +289,7 @@
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@@ -297,7 +299,7 @@
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@@ -309,7 +311,7 @@
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@@ -319,7 +321,7 @@
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@@ -331,7 +333,7 @@
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@@ -341,7 +343,7 @@
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@@ -353,7 +355,7 @@
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@@ -363,7 +365,7 @@
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@@ -383,7 +385,7 @@
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@@ -395,7 +397,7 @@
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@@ -405,7 +407,7 @@
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@@ -417,7 +419,7 @@
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@@ -429,7 +431,7 @@
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@@ -441,7 +443,7 @@
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@@ -453,7 +455,7 @@
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@@ -465,7 +467,7 @@
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@@ -477,7 +479,7 @@
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@@ -489,7 +491,7 @@
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@@ -501,7 +503,7 @@
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@@ -511,7 +513,7 @@
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@@ -523,7 +525,7 @@
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@@ -533,7 +535,7 @@
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@@ -552,7 +554,7 @@
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@@ -573,7 +575,7 @@
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@@ -585,7 +587,7 @@
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@@ -596,7 +598,7 @@
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@@ -609,7 +611,7 @@
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@@ -621,7 +623,7 @@
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@@ -631,7 +633,7 @@
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@@ -643,7 +645,7 @@
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@@ -653,7 +655,7 @@
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@@ -665,7 +667,7 @@
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@@ -675,7 +677,7 @@
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@@ -689,7 +691,7 @@
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@@ -701,7 +703,7 @@
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@@ -711,7 +713,7 @@
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@@ -723,7 +725,7 @@
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@@ -733,7 +735,7 @@
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@@ -745,7 +747,7 @@
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@@ -755,7 +757,7 @@
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@@ -767,7 +769,7 @@
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@@ -777,7 +779,7 @@
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@@ -793,7 +795,7 @@
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@@ -805,7 +807,7 @@
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@@ -815,7 +817,7 @@
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@@ -827,7 +829,7 @@
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@@ -840,7 +842,7 @@
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@@ -852,7 +854,7 @@
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@@ -862,7 +864,7 @@
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@@ -874,7 +876,7 @@
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@@ -884,7 +886,7 @@
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@@ -896,7 +898,7 @@
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@@ -908,7 +910,7 @@
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@@ -918,7 +920,7 @@
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@@ -930,7 +932,7 @@
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@@ -942,7 +944,7 @@
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@@ -954,7 +956,7 @@
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@@ -964,7 +966,7 @@
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@@ -976,7 +978,7 @@
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@@ -986,7 +988,7 @@
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@@ -1005,7 +1007,7 @@
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@@ -1033,7 +1035,7 @@
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@@ -1059,7 +1061,7 @@
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@@ -1076,7 +1078,7 @@
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@@ -1096,7 +1098,7 @@
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@@ -1125,7 +1127,7 @@
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@@ -1150,7 +1152,7 @@
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@@ -1170,7 +1172,7 @@
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@@ -1182,7 +1184,7 @@
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@@ -1192,7 +1194,7 @@
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@@ -1207,7 +1209,7 @@
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@@ -1220,7 +1222,7 @@
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@@ -1233,7 +1235,7 @@
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@@ -1245,7 +1247,7 @@
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@@ -1258,7 +1260,7 @@
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@@ -1269,7 +1271,7 @@
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@@ -1283,7 +1285,7 @@
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@@ -1293,7 +1295,7 @@
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@@ -1307,7 +1309,7 @@
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@@ -1320,7 +1322,7 @@
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@@ -1333,7 +1335,7 @@
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@@ -1343,7 +1345,7 @@
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@@ -1356,7 +1358,7 @@
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@@ -1366,7 +1368,7 @@
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@@ -1379,7 +1381,7 @@
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@@ -1391,7 +1393,7 @@
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@@ -1410,7 +1412,7 @@
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@@ -1423,7 +1425,7 @@
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@@ -1435,7 +1437,7 @@
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@@ -1448,7 +1450,7 @@
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@@ -1468,7 +1470,7 @@
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@@ -1491,7 +1493,7 @@
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@@ -1506,7 +1508,7 @@
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@@ -1518,7 +1520,7 @@
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@@ -1538,7 +1540,7 @@
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@@ -1558,7 +1560,7 @@
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@@ -1582,7 +1584,7 @@
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@@ -1603,7 +1605,7 @@
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@@ -1633,7 +1635,7 @@
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@@ -1657,7 +1659,7 @@
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    "execution_count": 1,
-   "id": "c1874811",
+   "id": "82f5a45c",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1669,7 +1671,7 @@
      "text": [
       "Bootstrap Statistics :\n",
       "original           bias      std. error\n",
-      " 100.135  15.1329        100.136        0.151103\n"
+      " 99.7217  14.8193        99.7194        0.147557\n"
      ]
     }
    ],
@@ -1706,7 +1708,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6dad6de5",
+   "id": "b1c292eb",
    "metadata": {
     "editable": true
    },
@@ -1716,7 +1718,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0342164e",
+   "id": "19a2ff64",
    "metadata": {
     "editable": true
    },
@@ -1727,7 +1729,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "0c0490cc",
+   "id": "0e3146d6",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1735,7 +1737,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1762,7 +1764,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0adf2510",
+   "id": "33a2920b",
    "metadata": {
     "editable": true
    },
@@ -1780,7 +1782,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "df226e05",
+   "id": "dcd7d41e",
    "metadata": {
     "editable": true
    },
@@ -1792,7 +1794,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c89ed73",
+   "id": "7a13a154",
    "metadata": {
     "editable": true
    },
@@ -1809,7 +1811,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "615aae51",
+   "id": "12c56a3e",
    "metadata": {
     "editable": true
    },
@@ -1821,7 +1823,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09df2f76",
+   "id": "ded9dfd0",
    "metadata": {
     "editable": true
    },
@@ -1831,7 +1833,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bc115459",
+   "id": "5c8be1fb",
    "metadata": {
     "editable": true
    },
@@ -1843,7 +1845,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a897d31c",
+   "id": "6ae6d83b",
    "metadata": {
     "editable": true
    },
@@ -1860,7 +1862,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f35ccec2",
+   "id": "ac6ad12e",
    "metadata": {
     "editable": true
    },
@@ -1872,7 +1874,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f78963d0",
+   "id": "24cd6a77",
    "metadata": {
     "editable": true
    },
@@ -1882,7 +1884,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5da42dc1",
+   "id": "82580456",
    "metadata": {
     "editable": true
    },
@@ -1894,7 +1896,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "681e6f51",
+   "id": "542a056a",
    "metadata": {
     "editable": true
    },
@@ -1904,7 +1906,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0cfeef23",
+   "id": "12d87c8f",
    "metadata": {
     "editable": true
    },
@@ -1916,7 +1918,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "543454fe",
+   "id": "661e392d",
    "metadata": {
     "editable": true
    },
@@ -1926,7 +1928,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9b876527",
+   "id": "c92115cc",
    "metadata": {
     "editable": true
    },
@@ -1942,7 +1944,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3c700f4e",
+   "id": "555ecab7",
    "metadata": {
     "editable": true
    },
@@ -1953,7 +1955,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "99d2acd5",
+   "id": "a1e3bf2d",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2043,7 +2045,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b41ce01c",
+   "id": "b05eafde",
    "metadata": {
     "editable": true
    },
@@ -2054,7 +2056,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "7c40879d",
+   "id": "23e711c6",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2078,7 +2080,13 @@
       "Error: 0.10398646080125035\n",
       "Bias^2: 0.1007711427354898\n",
       "Var: 0.0032153180657605116\n",
-      "0.10398646080125035 >= 0.1007711427354898 + 0.0032153180657605116 = 0.10398646080125032\n",
+      "0.10398646080125035 >= 0.1007711427354898 + 0.0032153180657605116 = 0.10398646080125032\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Polynomial degree: 3\n",
       "Error: 0.06547790180152355\n",
       "Bias^2: 0.06208238634231949\n",
@@ -2114,19 +2122,26 @@
       "Error: 0.017355848195593347\n",
       "Bias^2: 0.010331721306655127\n",
       "Var: 0.007024126888938232\n",
-      "0.017355848195593347 >= 0.010331721306655127 + 0.007024126888938232 = 0.01735584819559336\n"
+      "0.017355848195593347 >= 0.010331721306655127 + 0.007024126888938232 = 0.01735584819559336\n",
+      "Polynomial degree: 9\n",
+      "Error: 0.02660572763718093\n",
+      "Bias^2: 0.010018312644137363\n",
+      "Var: 0.016587414993043573\n",
+      "0.02660572763718093 >= 0.010018312644137363 + 0.016587414993043573 = 0.026605727637180936\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Polynomial degree: 9\n",
-      "Error: 0.02660572763718093\n",
-      "Bias^2: 0.010018312644137363\n",
-      "Var: 0.016587414993043573\n",
-      "0.02660572763718093 >= 0.010018312644137363 + 0.016587414993043573 = 0.026605727637180936\n",
-      "Polynomial degree: 10\n",
+      "Polynomial degree:"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 10\n",
       "Error: 0.021592704588025025\n",
       "Bias^2: 0.010516485576645508\n",
       "Var: 0.011076219011379514\n",
@@ -2135,13 +2150,7 @@
       "Error: 0.07160048164233104\n",
       "Bias^2: 0.014436800088904942\n",
       "Var: 0.05716368155342608\n",
-      "0.07160048164233104 >= 0.014436800088904942 + 0.05716368155342608 = 0.07160048164233102\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "0.07160048164233104 >= 0.014436800088904942 + 0.05716368155342608 = 0.07160048164233102\n",
       "Polynomial degree: 12\n",
       "Error: 0.11547777218872497\n",
       "Bias^2: 0.01628578269596628\n",
@@ -2163,7 +2172,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_139_4.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_139_5.png"
       }
      },
      "output_type": "display_data"
@@ -2220,7 +2229,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "494b741b",
+   "id": "d638746f",
    "metadata": {
     "editable": true
    },
@@ -2258,7 +2267,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "68d67d77",
+   "id": "6fe999c4",
    "metadata": {
     "editable": true
    },
@@ -2285,7 +2294,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "0b42fc93",
+   "id": "8289e153",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2362,7 +2371,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d4adf4c3",
+   "id": "3a9dffce",
    "metadata": {
     "editable": true
    },
@@ -2387,7 +2396,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6e6c3fd3",
+   "id": "2ee67263",
    "metadata": {
     "editable": true
    },
@@ -2415,7 +2424,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f56b418c",
+   "id": "c44868f2",
    "metadata": {
     "editable": true
    },
@@ -2428,7 +2437,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "64e72139",
+   "id": "df0c5466",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2543,7 +2552,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "92d3a119",
+   "id": "5cbfeb1f",
    "metadata": {
     "editable": true
    },
@@ -2554,7 +2563,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "20a55cbd",
+   "id": "34028f77",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2699,9 +2708,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_96719/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_405/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n",
       "  plt.plot(polynomial, np.log10(trainingerror), label='Training Error')\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_96719/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_405/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n",
       "  plt.plot(polynomial, np.log10(testerror), label='Test Error')\n"
      ]
     },
@@ -2803,7 +2812,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b1ab15d",
+   "id": "9c64885c",
    "metadata": {
     "editable": true
    },
@@ -2813,7 +2822,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d04d7a1a",
+   "id": "ff70b35f",
    "metadata": {
     "editable": true
    },
@@ -2826,7 +2835,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "bbd6bfa9",
+   "id": "e21b19fd",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2836,7 +2845,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_96719/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_405/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n",
       "  plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n"
      ]
     },
@@ -2927,7 +2936,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3d61d3cd",
+   "id": "cc43a51c",
    "metadata": {
     "editable": true
    },
@@ -2937,7 +2946,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "33014e05",
+   "id": "af5687ed",
    "metadata": {
     "editable": true
    },
@@ -2966,7 +2975,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "af8973e3",
+   "id": "47c3811a",
    "metadata": {
     "editable": true
    },
@@ -2982,7 +2991,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "95f08a4f",
+   "id": "980dac66",
    "metadata": {
     "editable": true
    },
@@ -3001,7 +3010,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "69a3772b",
+   "id": "be67d8c5",
    "metadata": {
     "editable": true
    },
@@ -3015,7 +3024,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6f44e2d9",
+   "id": "5133bf97",
    "metadata": {
     "editable": true
    },
@@ -3027,7 +3036,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9f820ddc",
+   "id": "b76cb41b",
    "metadata": {
     "editable": true
    },
@@ -3038,7 +3047,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cab623c2",
+   "id": "b6e9e9ce",
    "metadata": {
     "editable": true
    },
@@ -3050,7 +3059,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "47218f25",
+   "id": "53479926",
    "metadata": {
     "editable": true
    },
@@ -3062,7 +3071,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9290dce4",
+   "id": "1929fd98",
    "metadata": {
     "editable": true
    },
@@ -3078,7 +3087,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "65a7ab7d",
+   "id": "18b53cb3",
    "metadata": {
     "editable": true
    },
@@ -3089,7 +3098,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a35cbd7b",
+   "id": "a57528a8",
    "metadata": {
     "editable": true
    },
@@ -3112,7 +3121,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b5e006f",
+   "id": "b5e5c863",
    "metadata": {
     "editable": true
    },
@@ -3123,7 +3132,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "77270968",
+   "id": "13fe1b50",
    "metadata": {
     "editable": true
    },
@@ -3135,7 +3144,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "49715a2d",
+   "id": "22327251",
    "metadata": {
     "editable": true
    },
@@ -3147,7 +3156,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fb6a34f4",
+   "id": "f7875671",
    "metadata": {
     "editable": true
    },
@@ -3161,7 +3170,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "942e7e6a",
+   "id": "584d9150",
    "metadata": {
     "editable": true
    },
@@ -3192,7 +3201,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f127bfb2",
+   "id": "0ebb2d20",
    "metadata": {
     "editable": true
    },
@@ -3214,7 +3223,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2def67b5",
+   "id": "b701ef3f",
    "metadata": {
     "editable": true
    },
@@ -3226,7 +3235,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d28e3d89",
+   "id": "1453c4df",
    "metadata": {
     "editable": true
    },
@@ -3239,7 +3248,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ab34b80c",
+   "id": "fade84b6",
    "metadata": {
     "editable": true
    },
@@ -3251,7 +3260,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "677844a3",
+   "id": "e91b254f",
    "metadata": {
     "editable": true
    },
@@ -3263,7 +3272,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "89529d82",
+   "id": "6f50fb28",
    "metadata": {
     "editable": true
    },
@@ -3275,7 +3284,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "688cfbfa",
+   "id": "e37b2bfb",
    "metadata": {
     "editable": true
    },
diff --git a/doc/LectureNotes/_build/jupyter_execute/week37.py b/doc/LectureNotes/_build/jupyter_execute/week37.py
index 9e1d1126a..21ff6edee 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week37.py
+++ b/doc/LectureNotes/_build/jupyter_execute/week37.py
@@ -47,6 +47,8 @@
 # 
 #   * Work on project 1
 # 
+#   * [Video of exercise sessions week 37](https://youtu.be/bK4AEcTu-oM)
+# 
 #   * For more discussions of Ridge regression and calculation of averages, [Wessel van Wieringen's](https://arxiv.org/abs/1509.09169) article is highly recommended.
 
 # ## Material for lecture Monday September 9
diff --git a/doc/LectureNotes/_build/jupyter_execute/week37_121_0.png b/doc/LectureNotes/_build/jupyter_execute/week37_121_0.png
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diff --git a/doc/LectureNotes/week37.ipynb b/doc/LectureNotes/week37.ipynb
index 70763a9b5..c611a8dbf 100644
--- a/doc/LectureNotes/week37.ipynb
+++ b/doc/LectureNotes/week37.ipynb
@@ -2,7 +2,7 @@
  "cells": [
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    "cell_type": "markdown",
-   "id": "53365fbb",
+   "id": "7ce0c2c8",
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@@ -14,7 +14,7 @@
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@@ -29,7 +29,7 @@
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@@ -60,7 +60,7 @@
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@@ -77,12 +77,14 @@
     "\n",
     "  * Work on project 1\n",
     "\n",
+    "  * [Video of exercise sessions week 37](https://youtu.be/bK4AEcTu-oM)\n",
+    "\n",
     "  * For more discussions of Ridge regression and calculation of averages, [Wessel van Wieringen's](https://arxiv.org/abs/1509.09169) article is highly recommended."
    ]
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@@ -199,7 +201,7 @@
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@@ -222,7 +224,7 @@
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@@ -489,7 +491,7 @@
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@@ -501,7 +503,7 @@
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@@ -1059,7 +1061,7 @@
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@@ -1076,7 +1078,7 @@
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@@ -1096,7 +1098,7 @@
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@@ -1125,7 +1127,7 @@
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@@ -1150,7 +1152,7 @@
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@@ -1170,7 +1172,7 @@
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@@ -1182,7 +1184,7 @@
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@@ -1192,7 +1194,7 @@
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@@ -1207,7 +1209,7 @@
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@@ -1220,7 +1222,7 @@
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@@ -1233,7 +1235,7 @@
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@@ -1245,7 +1247,7 @@
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@@ -1258,7 +1260,7 @@
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@@ -1269,7 +1271,7 @@
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@@ -1283,7 +1285,7 @@
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@@ -1293,7 +1295,7 @@
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@@ -1307,7 +1309,7 @@
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@@ -1320,7 +1322,7 @@
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@@ -1333,7 +1335,7 @@
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@@ -1343,7 +1345,7 @@
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@@ -1356,7 +1358,7 @@
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@@ -1366,7 +1368,7 @@
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@@ -1379,7 +1381,7 @@
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@@ -1391,7 +1393,7 @@
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@@ -1410,7 +1412,7 @@
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@@ -1423,7 +1425,7 @@
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@@ -1435,7 +1437,7 @@
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@@ -1448,7 +1450,7 @@
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@@ -1468,7 +1470,7 @@
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@@ -1491,7 +1493,7 @@
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@@ -1506,7 +1508,7 @@
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@@ -1518,7 +1520,7 @@
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@@ -1538,7 +1540,7 @@
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@@ -1558,7 +1560,7 @@
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@@ -1582,7 +1584,7 @@
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@@ -1603,7 +1605,7 @@
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@@ -1633,7 +1635,7 @@
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@@ -1657,7 +1659,7 @@
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@@ -1696,7 +1698,7 @@
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@@ -1706,7 +1708,7 @@
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@@ -1717,7 +1719,7 @@
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@@ -1737,7 +1739,7 @@
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@@ -1755,7 +1757,7 @@
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@@ -1767,7 +1769,7 @@
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@@ -1784,7 +1786,7 @@
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@@ -1796,7 +1798,7 @@
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@@ -1806,7 +1808,7 @@
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@@ -1818,7 +1820,7 @@
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@@ -1835,7 +1837,7 @@
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@@ -1847,7 +1849,7 @@
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@@ -1857,7 +1859,7 @@
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@@ -1869,7 +1871,7 @@
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@@ -1879,7 +1881,7 @@
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@@ -1891,7 +1893,7 @@
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@@ -1901,7 +1903,7 @@
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@@ -1917,7 +1919,7 @@
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@@ -1928,7 +1930,7 @@
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@@ -1993,7 +1995,7 @@
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@@ -2004,7 +2006,7 @@
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@@ -2061,7 +2063,7 @@
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@@ -2099,7 +2101,7 @@
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@@ -2126,7 +2128,7 @@
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@@ -2188,7 +2190,7 @@
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@@ -2213,7 +2215,7 @@
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@@ -2241,7 +2243,7 @@
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@@ -2254,7 +2256,7 @@
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@@ -2354,7 +2356,7 @@
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@@ -2365,7 +2367,7 @@
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@@ -2454,7 +2456,7 @@
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@@ -2464,7 +2466,7 @@
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@@ -2477,7 +2479,7 @@
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@@ -2555,7 +2557,7 @@
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@@ -2565,7 +2567,7 @@
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@@ -2594,7 +2596,7 @@
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@@ -2610,7 +2612,7 @@
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@@ -2629,7 +2631,7 @@
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@@ -2643,7 +2645,7 @@
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@@ -2655,7 +2657,7 @@
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@@ -2666,7 +2668,7 @@
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@@ -2678,7 +2680,7 @@
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@@ -2690,7 +2692,7 @@
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@@ -2706,7 +2708,7 @@
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@@ -2717,7 +2719,7 @@
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@@ -2740,7 +2742,7 @@
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@@ -2751,7 +2753,7 @@
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@@ -2763,7 +2765,7 @@
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@@ -2775,7 +2777,7 @@
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@@ -2789,7 +2791,7 @@
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@@ -2820,7 +2822,7 @@
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@@ -2842,7 +2844,7 @@
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@@ -2854,7 +2856,7 @@
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@@ -2867,7 +2869,7 @@
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@@ -2879,7 +2881,7 @@
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@@ -2891,7 +2893,7 @@
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@@ -2903,7 +2905,7 @@
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