diff --git a/doc/pub/week34/html/._week34-bs000.html b/doc/pub/week34/html/._week34-bs000.html
index 6c6ead9e7..20af4c181 100644
--- a/doc/pub/week34/html/._week34-bs000.html
+++ b/doc/pub/week34/html/._week34-bs000.html
@@ -377,7 +377,7 @@ MathJax.Hub.Config({
 </center>
 <br>
 <center>
-<h4>Week 34, August 21-25, 2023</h4>
+<h4>Week 34, August 19-23, 2024</h4>
 </center> <!-- date -->
 <br>
 
diff --git a/doc/pub/week34/html/._week34-bs001.html b/doc/pub/week34/html/._week34-bs001.html
index 3b2428907..0f2dc1e2a 100644
--- a/doc/pub/week34/html/._week34-bs001.html
+++ b/doc/pub/week34/html/._week34-bs001.html
@@ -369,9 +369,9 @@ MathJax.Hub.Config({
   <li> Tuesdays 815am-12pm and 1215pm-4pm</li>
   <li> Wednesdays 815am-12pm and 1215pm-4pm.</li>
 </ul>
-<li> On Thursdays we have a regular lecture. These lectures start at 1215pm and end at 2pm and serve the aims of giving an overview over various topics. These lectures will also be recorded.</li>
+<li> On Mondays  we have a regular lecture which will be organized as a mix of active learning sessions and lecturing. These lectures/active learning sessions start at 1015am and end at 12pm and serve the aims of giving an overview over various topics. These lectures will also be recorded.</li>
 <ul>
-  <li> <a href="https://youtu.be/OAslWa-NIkQ" target="_self">Link to recording of lecture</a></li>
+  <li> <a href="https://youtu.be/" target="_self">Link to recording of lecture TBA</a></li>
 </ul>
 </ol>
 <p>The labs are also available till 6pm Tuesdays and Wednesdays. Videos and learning material with reading suggestions will be made available before each week starts.</p>
diff --git a/doc/pub/week34/html/._week34-bs002.html b/doc/pub/week34/html/._week34-bs002.html
index 1146da834..d6eb362b8 100644
--- a/doc/pub/week34/html/._week34-bs002.html
+++ b/doc/pub/week34/html/._week34-bs002.html
@@ -365,9 +365,9 @@ MathJax.Hub.Config({
 <div class="panel-body">
 <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
 <ul>
-  <li> August 22: Presentation of the course, aims and content. Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.</li>
-  <li> August 23: Presentation of the course, aims and content. Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.</li>
-  <li> August 24: Lecture: Linear regression, examples and theory</li> 
+  <li> August 19: Lecture: Presentation of course, Linear regression, examples and theory</li> 
+  <li> August 20: Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.</li>
+  <li> August 23: Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.</li>
 </ul>
 </div>
 </div>
diff --git a/doc/pub/week34/html/._week34-bs003.html b/doc/pub/week34/html/._week34-bs003.html
index 3927c5643..57ebde31f 100644
--- a/doc/pub/week34/html/._week34-bs003.html
+++ b/doc/pub/week34/html/._week34-bs003.html
@@ -365,8 +365,8 @@ MathJax.Hub.Config({
 <div class="panel-body">
 <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
 <ul>
+  <li> Mondays: regular lectures/active lerning sessions (10.15am-12pm)</li> 
   <li> The sessions on Tuesdays and Wednesdays last four hours and will include partly lectures and discussions in the beginning.</li>
-  <li> Thursdays: regular lectures (12.15pm-2pm)</li> 
   <li> Weekly reading assignments and videos needed to solve projects and exercises.</li>
   <li> Weekly exercises. You can hand in exercises if you want and get an extra score, see below.</li>
   <li> Detailed lecture notes, exercises, all programs presented, projects etc can be found at the homepage of the course.</li>
diff --git a/doc/pub/week34/html/._week34-bs006.html b/doc/pub/week34/html/._week34-bs006.html
index 40431861a..a54c9a02f 100644
--- a/doc/pub/week34/html/._week34-bs006.html
+++ b/doc/pub/week34/html/._week34-bs006.html
@@ -372,8 +372,8 @@ MathJax.Hub.Config({
 <li> Fahimeh Najafi, fahimeh.najafi@fys.uio.no</li>
 <li> Mia-Katrin Ose Kvalsund, m.k.o.kvalsund@fys.uio.no</li>
 <li> Karl Henrik Fredly, k.h.fredly@fys.uio.no</li>
-<li> Adam Jakobsen, adam.jakobsen@fys.uio.no</li>
-<li> Daniel Haas Beccatini Lima, d.h.b.lima@fys.uio.no</li>
+<li> TBA</li>
+<li> TBA</li>
 </ul>
 <p>
 <!-- navigation buttons at the bottom of the page -->
diff --git a/doc/pub/week34/html/week34-bs.html b/doc/pub/week34/html/week34-bs.html
index 6c6ead9e7..20af4c181 100644
--- a/doc/pub/week34/html/week34-bs.html
+++ b/doc/pub/week34/html/week34-bs.html
@@ -377,7 +377,7 @@ MathJax.Hub.Config({
 </center>
 <br>
 <center>
-<h4>Week 34, August 21-25, 2023</h4>
+<h4>Week 34, August 19-23, 2024</h4>
 </center> <!-- date -->
 <br>
 
diff --git a/doc/pub/week34/html/week34-reveal.html b/doc/pub/week34/html/week34-reveal.html
index fb54cbcbf..5a661f73f 100644
--- a/doc/pub/week34/html/week34-reveal.html
+++ b/doc/pub/week34/html/week34-reveal.html
@@ -184,7 +184,7 @@ MathJax.Hub.Config({
 </center>
 <br>
 <center>
-<h4>Week 34, August 21-25, 2023</h4>
+<h4>Week 34, August 19-23, 2024</h4>
 </center> <!-- date -->
 <br>
 
@@ -208,10 +208,10 @@ MathJax.Hub.Config({
 <p><li> Wednesdays 815am-12pm and 1215pm-4pm.</li>
 </ul>
 <p>
-<p><li> On Thursdays we have a regular lecture. These lectures start at 1215pm and end at 2pm and serve the aims of giving an overview over various topics. These lectures will also be recorded.</li>
+<p><li> On Mondays  we have a regular lecture which will be organized as a mix of active learning sessions and lecturing. These lectures/active learning sessions start at 1015am and end at 12pm and serve the aims of giving an overview over various topics. These lectures will also be recorded.</li>
 <ul>
 
-<p><li> <a href="https://youtu.be/OAslWa-NIkQ" target="_blank">Link to recording of lecture</a></li>
+<p><li> <a href="https://youtu.be/" target="_blank">Link to recording of lecture TBA</a></li>
 </ul>
 <p>
 </ol>
@@ -227,11 +227,11 @@ MathJax.Hub.Config({
 <p>
 <ul>
 
-<p><li> August 22: Presentation of the course, aims and content. Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.</li>
+<p><li> August 19: Lecture: Presentation of course, Linear regression, examples and theory</li>
 
-<p><li> August 23: Presentation of the course, aims and content. Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.</li>
+<p><li> August 20: Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.</li>
 
-<p><li> August 24: Lecture: Linear regression, examples and theory</li> 
+<p><li> August 23: Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.</li>
 </ul>
 </div>
 </section>
@@ -244,9 +244,9 @@ MathJax.Hub.Config({
 <p>
 <ul>
 
-<p><li> The sessions on Tuesdays and Wednesdays last four hours and will include partly lectures and discussions in the beginning.</li>
+<p><li> Mondays: regular lectures/active lerning sessions (10.15am-12pm)</li>
 
-<p><li> Thursdays: regular lectures (12.15pm-2pm)</li>
+<p><li> The sessions on Tuesdays and Wednesdays last four hours and will include partly lectures and discussions in the beginning.</li>
 
 <p><li> Weekly reading assignments and videos needed to solve projects and exercises.</li>
 
@@ -310,8 +310,8 @@ MathJax.Hub.Config({
 <p><li> Fahimeh Najafi, fahimeh.najafi@fys.uio.no</li>
 <p><li> Mia-Katrin Ose Kvalsund, m.k.o.kvalsund@fys.uio.no</li>
 <p><li> Karl Henrik Fredly, k.h.fredly@fys.uio.no</li>
-<p><li> Adam Jakobsen, adam.jakobsen@fys.uio.no</li>
-<p><li> Daniel Haas Beccatini Lima, d.h.b.lima@fys.uio.no</li>
+<p><li> TBA</li>
+<p><li> TBA</li>
 </ul>
 </section>
 
diff --git a/doc/pub/week34/html/week34-solarized.html b/doc/pub/week34/html/week34-solarized.html
index 77c2104e6..3d551558b 100644
--- a/doc/pub/week34/html/week34-solarized.html
+++ b/doc/pub/week34/html/week34-solarized.html
@@ -305,7 +305,7 @@ MathJax.Hub.Config({
 </center>
 <br>
 <center>
-<h4>Week 34, August 21-25, 2023</h4>
+<h4>Week 34, August 19-23, 2024</h4>
 </center> <!-- date -->
 <br>
 
@@ -320,9 +320,9 @@ MathJax.Hub.Config({
   <li> Tuesdays 815am-12pm and 1215pm-4pm</li>
   <li> Wednesdays 815am-12pm and 1215pm-4pm.</li>
 </ul>
-<li> On Thursdays we have a regular lecture. These lectures start at 1215pm and end at 2pm and serve the aims of giving an overview over various topics. These lectures will also be recorded.</li>
+<li> On Mondays  we have a regular lecture which will be organized as a mix of active learning sessions and lecturing. These lectures/active learning sessions start at 1015am and end at 12pm and serve the aims of giving an overview over various topics. These lectures will also be recorded.</li>
 <ul>
-  <li> <a href="https://youtu.be/OAslWa-NIkQ" target="_blank">Link to recording of lecture</a></li>
+  <li> <a href="https://youtu.be/" target="_blank">Link to recording of lecture TBA</a></li>
 </ul>
 </ol>
 <p>The labs are also available till 6pm Tuesdays and Wednesdays. Videos and learning material with reading suggestions will be made available before each week starts.</p>
@@ -334,9 +334,9 @@ MathJax.Hub.Config({
 <b></b>
 <p>
 <ul>
-  <li> August 22: Presentation of the course, aims and content. Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.</li>
-  <li> August 23: Presentation of the course, aims and content. Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.</li>
-  <li> August 24: Lecture: Linear regression, examples and theory</li> 
+  <li> August 19: Lecture: Presentation of course, Linear regression, examples and theory</li> 
+  <li> August 20: Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.</li>
+  <li> August 23: Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.</li>
 </ul>
 </div>
 
@@ -348,8 +348,8 @@ MathJax.Hub.Config({
 <b></b>
 <p>
 <ul>
+  <li> Mondays: regular lectures/active lerning sessions (10.15am-12pm)</li> 
   <li> The sessions on Tuesdays and Wednesdays last four hours and will include partly lectures and discussions in the beginning.</li>
-  <li> Thursdays: regular lectures (12.15pm-2pm)</li> 
   <li> Weekly reading assignments and videos needed to solve projects and exercises.</li>
   <li> Weekly exercises. You can hand in exercises if you want and get an extra score, see below.</li>
   <li> Detailed lecture notes, exercises, all programs presented, projects etc can be found at the homepage of the course.</li>
@@ -393,8 +393,8 @@ MathJax.Hub.Config({
 <li> Fahimeh Najafi, fahimeh.najafi@fys.uio.no</li>
 <li> Mia-Katrin Ose Kvalsund, m.k.o.kvalsund@fys.uio.no</li>
 <li> Karl Henrik Fredly, k.h.fredly@fys.uio.no</li>
-<li> Adam Jakobsen, adam.jakobsen@fys.uio.no</li>
-<li> Daniel Haas Beccatini Lima, d.h.b.lima@fys.uio.no</li>
+<li> TBA</li>
+<li> TBA</li>
 </ul>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="deadlines-for-projects-tentative">Deadlines for projects (tentative) </h2>
diff --git a/doc/pub/week34/html/week34.html b/doc/pub/week34/html/week34.html
index 7d5f604f0..3a0141f80 100644
--- a/doc/pub/week34/html/week34.html
+++ b/doc/pub/week34/html/week34.html
@@ -382,7 +382,7 @@ MathJax.Hub.Config({
 </center>
 <br>
 <center>
-<h4>Week 34, August 21-25, 2023</h4>
+<h4>Week 34, August 19-23, 2024</h4>
 </center> <!-- date -->
 <br>
 
@@ -397,9 +397,9 @@ MathJax.Hub.Config({
   <li> Tuesdays 815am-12pm and 1215pm-4pm</li>
   <li> Wednesdays 815am-12pm and 1215pm-4pm.</li>
 </ul>
-<li> On Thursdays we have a regular lecture. These lectures start at 1215pm and end at 2pm and serve the aims of giving an overview over various topics. These lectures will also be recorded.</li>
+<li> On Mondays  we have a regular lecture which will be organized as a mix of active learning sessions and lecturing. These lectures/active learning sessions start at 1015am and end at 12pm and serve the aims of giving an overview over various topics. These lectures will also be recorded.</li>
 <ul>
-  <li> <a href="https://youtu.be/OAslWa-NIkQ" target="_blank">Link to recording of lecture</a></li>
+  <li> <a href="https://youtu.be/" target="_blank">Link to recording of lecture TBA</a></li>
 </ul>
 </ol>
 <p>The labs are also available till 6pm Tuesdays and Wednesdays. Videos and learning material with reading suggestions will be made available before each week starts.</p>
@@ -411,9 +411,9 @@ MathJax.Hub.Config({
 <b></b>
 <p>
 <ul>
-  <li> August 22: Presentation of the course, aims and content. Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.</li>
-  <li> August 23: Presentation of the course, aims and content. Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.</li>
-  <li> August 24: Lecture: Linear regression, examples and theory</li> 
+  <li> August 19: Lecture: Presentation of course, Linear regression, examples and theory</li> 
+  <li> August 20: Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.</li>
+  <li> August 23: Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.</li>
 </ul>
 </div>
 
@@ -425,8 +425,8 @@ MathJax.Hub.Config({
 <b></b>
 <p>
 <ul>
+  <li> Mondays: regular lectures/active lerning sessions (10.15am-12pm)</li> 
   <li> The sessions on Tuesdays and Wednesdays last four hours and will include partly lectures and discussions in the beginning.</li>
-  <li> Thursdays: regular lectures (12.15pm-2pm)</li> 
   <li> Weekly reading assignments and videos needed to solve projects and exercises.</li>
   <li> Weekly exercises. You can hand in exercises if you want and get an extra score, see below.</li>
   <li> Detailed lecture notes, exercises, all programs presented, projects etc can be found at the homepage of the course.</li>
@@ -470,8 +470,8 @@ MathJax.Hub.Config({
 <li> Fahimeh Najafi, fahimeh.najafi@fys.uio.no</li>
 <li> Mia-Katrin Ose Kvalsund, m.k.o.kvalsund@fys.uio.no</li>
 <li> Karl Henrik Fredly, k.h.fredly@fys.uio.no</li>
-<li> Adam Jakobsen, adam.jakobsen@fys.uio.no</li>
-<li> Daniel Haas Beccatini Lima, d.h.b.lima@fys.uio.no</li>
+<li> TBA</li>
+<li> TBA</li>
 </ul>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="deadlines-for-projects-tentative">Deadlines for projects (tentative) </h2>
diff --git a/doc/pub/week34/ipynb/ipynb-week34-src.tar.gz b/doc/pub/week34/ipynb/ipynb-week34-src.tar.gz
index 59a468d63..e5465c981 100644
Binary files a/doc/pub/week34/ipynb/ipynb-week34-src.tar.gz and b/doc/pub/week34/ipynb/ipynb-week34-src.tar.gz differ
diff --git a/doc/pub/week34/ipynb/week34.ipynb b/doc/pub/week34/ipynb/week34.ipynb
index 9ad6b53e9..f5b04d759 100644
--- a/doc/pub/week34/ipynb/week34.ipynb
+++ b/doc/pub/week34/ipynb/week34.ipynb
@@ -2,8 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "5fe092d5",
-   "metadata": {},
+   "id": "38e15e02",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)\n",
     "doconce format html week34.do.txt --no_mako -->\n",
@@ -12,19 +14,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "67aa2fe6",
-   "metadata": {},
+   "id": "2f3ec945",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "# Week 34: Introduction to the course, Logistics and Practicalities\n",
     "**Morten Hjorth-Jensen**, Department of Physics and Center for Computing in Science Education, University of Oslo, Norway and Department of Physics and Astronomy and Facility for Rare Isotope Beams, Michigan State University, USA\n",
     "\n",
-    "Date: **Week 34, August 21-25, 2023**"
+    "Date: **Week 34, August 19-23, 2024**"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fba11718",
-   "metadata": {},
+   "id": "784b1772",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Overview of first week\n",
     "\n",
@@ -38,37 +44,41 @@
     "\n",
     "  * Wednesdays 815am-12pm and 1215pm-4pm.\n",
     "\n",
-    "4. On Thursdays we have a regular lecture. These lectures start at 1215pm and end at 2pm and serve the aims of giving an overview over various topics. These lectures will also be recorded.\n",
+    "4. On Mondays  we have a regular lecture which will be organized as a mix of active learning sessions and lecturing. These lectures/active learning sessions start at 1015am and end at 12pm and serve the aims of giving an overview over various topics. These lectures will also be recorded.\n",
     "\n",
-    "  * [Link to recording of lecture](https://youtu.be/OAslWa-NIkQ)\n",
+    "  * [Link to recording of lecture TBA](https://youtu.be/)\n",
     "\n",
     "The labs are also available till 6pm Tuesdays and Wednesdays. Videos and learning material with reading suggestions will be made available before each week starts."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "cfdcf25b",
-   "metadata": {},
+   "id": "e3701340",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Schedule first week\n",
     "\n",
-    "  * August 22: Presentation of the course, aims and content. Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.\n",
+    "  * August 19: Lecture: Presentation of course, Linear regression, examples and theory \n",
     "\n",
-    "  * August 23: Presentation of the course, aims and content. Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.\n",
+    "  * August 20: Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.\n",
     "\n",
-    "  * August 24: Lecture: Linear regression, examples and theory"
+    "  * August 23: Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c77d7fae",
-   "metadata": {},
+   "id": "be9c9995",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Lectures and ComputerLab\n",
     "\n",
-    "  * The sessions on Tuesdays and Wednesdays last four hours and will include partly lectures and discussions in the beginning.\n",
+    "  * Mondays: regular lectures/active lerning sessions (10.15am-12pm) \n",
     "\n",
-    "  * Thursdays: regular lectures (12.15pm-2pm) \n",
+    "  * The sessions on Tuesdays and Wednesdays last four hours and will include partly lectures and discussions in the beginning.\n",
     "\n",
     "  * Weekly reading assignments and videos needed to solve projects and exercises.\n",
     "\n",
@@ -83,8 +93,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3738a09a",
-   "metadata": {},
+   "id": "00385125",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Communication channels\n",
     "\n",
@@ -95,8 +107,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c6fa34ac",
-   "metadata": {},
+   "id": "38c11cfa",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Course Format\n",
     "\n",
@@ -115,8 +129,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a3c141bc",
-   "metadata": {},
+   "id": "b7e4a9b8",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Teachers\n",
     "\n",
@@ -136,15 +152,17 @@
     "\n",
     "* Karl Henrik Fredly, k.h.fredly@fys.uio.no\n",
     "\n",
-    "* Adam Jakobsen, adam.jakobsen@fys.uio.no\n",
+    "* TBA\n",
     "\n",
-    "* Daniel Haas Beccatini Lima, d.h.b.lima@fys.uio.no"
+    "* TBA"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "827278be",
-   "metadata": {},
+   "id": "153ac614",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Deadlines for projects (tentative)\n",
     "\n",
@@ -159,8 +177,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d53281cb",
-   "metadata": {},
+   "id": "fa23c81b",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Grading\n",
     "\n",
@@ -185,8 +205,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4858de81",
-   "metadata": {},
+   "id": "714b3508",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Reading material\n",
     "\n",
@@ -197,8 +219,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2794a39e",
-   "metadata": {},
+   "id": "e4eb6dd8",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Textbooks\n",
     "\n",
@@ -215,8 +239,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "acacb7e9",
-   "metadata": {},
+   "id": "e223ecc1",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Reading suggestions week 34\n",
     "\n",
@@ -225,8 +251,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c4c7abf",
-   "metadata": {},
+   "id": "d9d7da4e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Prerequisites\n",
     "\n",
@@ -243,8 +271,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d67ca9c2",
-   "metadata": {},
+   "id": "7bccdc87",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Topics covered in this course: Statistical analysis and optimization of data\n",
     "\n",
@@ -259,8 +289,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c0fea0bc",
-   "metadata": {},
+   "id": "f9d4cb0e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Statistical analysis and optimization of data\n",
     "\n",
@@ -282,8 +314,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d22450f5",
-   "metadata": {},
+   "id": "1e4048d8",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Machine Learning\n",
     "\n",
@@ -316,8 +350,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8b088de5",
-   "metadata": {},
+   "id": "491df679",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Extremely useful tools, strongly recommended\n",
     "\n",
@@ -330,8 +366,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "feeb147e",
-   "metadata": {},
+   "id": "9b1e5150",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Other courses on Data science and Machine Learning  at UiO\n",
     "\n",
@@ -350,8 +388,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "281f9d12",
-   "metadata": {},
+   "id": "2da20e2e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Other courses on Data science and Machine Learning  at UiO, contn\n",
     "\n",
@@ -366,8 +406,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6089ee03",
-   "metadata": {},
+   "id": "25745025",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Learning outcomes\n",
     "\n",
@@ -408,8 +450,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "43cb57c4",
-   "metadata": {},
+   "id": "a5766b27",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Introduction\n",
     "\n",
@@ -449,8 +493,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3095b646",
-   "metadata": {},
+   "id": "36e3012d",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## AI/ML and some statements you may have heard (and what do they mean?)\n",
     "\n",
@@ -467,8 +513,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c60f6e2e",
-   "metadata": {},
+   "id": "993209f8",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## What is Machine Learning?\n",
     "\n",
@@ -538,8 +586,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "72352b1b",
-   "metadata": {},
+   "id": "ce7890fe",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Types of Machine Learning\n",
     "\n",
@@ -565,8 +615,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "23ba629e",
-   "metadata": {},
+   "id": "df7153ad",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Essential elements of ML\n",
     "\n",
@@ -581,8 +633,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c599f39",
-   "metadata": {},
+   "id": "9df68e7f",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## An optimization/minimization problem\n",
     "\n",
@@ -591,8 +645,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cf9b9e5d",
-   "metadata": {},
+   "id": "6b4d63d7",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## A Frequentist approach to data analysis\n",
     "\n",
@@ -624,8 +680,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3b06e150",
-   "metadata": {},
+   "id": "d7ec3ef2",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## What is a good model?\n",
     "\n",
@@ -652,8 +710,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bf856150",
-   "metadata": {},
+   "id": "e1c94627",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## What is a good model? Can we define it?\n",
     "\n",
@@ -681,8 +741,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9280cb97",
-   "metadata": {},
+   "id": "7d797904",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Software and needed installations\n",
     "\n",
@@ -718,8 +780,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d98c4ad1",
-   "metadata": {},
+   "id": "373e5431",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Python installers\n",
     "\n",
@@ -749,8 +813,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "733bd5e0",
-   "metadata": {},
+   "id": "82d18d72",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Useful Python libraries\n",
     "Here we list several useful Python libraries we strongly recommend (if you use anaconda many of these are already there)\n",
@@ -782,8 +848,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d8d2a176",
-   "metadata": {},
+   "id": "56213885",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Installing R, C++, cython or Julia\n",
     "\n",
@@ -804,8 +872,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7ded2900",
-   "metadata": {},
+   "id": "5ecb1897",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Installing R, C++, cython, Numba etc\n",
     "\n",
@@ -829,16 +899,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "21d076bb",
-   "metadata": {},
+   "id": "99fa58db",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "        pycod jupyter nbconvert filename.ipynb --to latex \n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "88fa958d",
-   "metadata": {},
+   "id": "888f4ad7",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "And to add more versatility, the Python package [SymPy](http://www.sympy.org/en/index.html) is a Python library for symbolic mathematics. It aims to become a full-featured computer algebra system (CAS)  and is entirely written in Python. \n",
     "\n",
@@ -847,8 +921,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eec51ca3",
-   "metadata": {},
+   "id": "a72f19de",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Numpy examples and Important Matrix and vector handling packages\n",
     "\n",
@@ -866,8 +942,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9ef0542a",
-   "metadata": {},
+   "id": "a07639b8",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Numpy and arrays\n",
     "[Numpy](http://www.numpy.org/) provides an easy way to handle arrays in Python. The standard way to import this library is as"
@@ -876,8 +954,11 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "5cb94529",
-   "metadata": {},
+   "id": "e660d6fe",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "import numpy as np"
@@ -885,8 +966,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8233194e",
-   "metadata": {},
+   "id": "154092cd",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "Here follows a simple example where we set up an array of ten elements, all determined by random numbers drawn according to the normal distribution,"
    ]
@@ -894,18 +977,12 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "af203786",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 1.26308787  1.13549158 -0.38961136  1.14547226 -0.12244053 -0.90615052\n",
-      " -0.86514806 -0.28072716 -1.74251207  0.34150908]\n"
-     ]
-    }
-   ],
+   "id": "a8934e81",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "n = 10\n",
     "x = np.random.normal(size=n)\n",
@@ -914,8 +991,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c2c53c39",
-   "metadata": {},
+   "id": "d23bef12",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "We defined a vector $x$ with $n=10$ elements with its values given by the Normal distribution $N(0,1)$.\n",
     "Another alternative is to declare a vector as follows"
@@ -924,17 +1003,12 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "afeb7fbd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1 2 3]\n"
-     ]
-    }
-   ],
+   "id": "07bff056",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "x = np.array([1, 2, 3])\n",
@@ -943,8 +1017,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d5bbfe6e",
-   "metadata": {},
+   "id": "f7cb8625",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "Here we have defined a vector with three elements, with $x_0=1$, $x_1=2$ and $x_2=3$. Note that both Python and C++\n",
     "start numbering array elements from $0$ and on. This means that a vector with $n$ elements has a sequence of entities $x_0, x_1, x_2, \\dots, x_{n-1}$. We could also let (recommended) Numpy to compute the logarithms of a specific array as"
@@ -953,17 +1029,12 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "774b9f59",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1.38629436 1.94591015 2.07944154]\n"
-     ]
-    }
-   ],
+   "id": "f8df173c",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "x = np.log(np.array([4, 7, 8]))\n",
@@ -972,8 +1043,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bab33f61",
-   "metadata": {},
+   "id": "8fa8744b",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "In the last example we used Numpy's unary function $np.log$. This function is\n",
     "highly tuned to compute array elements since the code is vectorized\n",
@@ -987,17 +1060,12 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "e50d9d86",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1 1 2]\n"
-     ]
-    }
-   ],
+   "id": "0e224446",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "from math import log\n",
@@ -1009,8 +1077,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "26162cbc",
-   "metadata": {},
+   "id": "56159b9e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "We note that our code is much longer already and we need to import the **log** function from the **math** module. \n",
     "The attentive reader will also notice that the output is $[1, 1, 2]$. Python interprets automagically our numbers as integers (like the **automatic** keyword in C++). To change this we could define our array elements to be double precision numbers as"
@@ -1019,17 +1089,12 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "2939cbb6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1.38629436 1.94591015 2.07944154]\n"
-     ]
-    }
-   ],
+   "id": "128087d3",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "x = np.log(np.array([4, 7, 8], dtype = np.float64))\n",
@@ -1038,8 +1103,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7cb3d794",
-   "metadata": {},
+   "id": "022f5010",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "or simply write them as double precision numbers (Python uses 64 bits as default for floating point type variables), that is"
    ]
@@ -1047,17 +1114,12 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "c5a3c7de",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1.38629436 1.94591015 2.07944154]\n"
-     ]
-    }
-   ],
+   "id": "d9150838",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "x = np.log(np.array([4.0, 7.0, 8.0]))\n",
@@ -1066,8 +1128,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "39be4ece",
-   "metadata": {},
+   "id": "7e203018",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "To check the number of bytes (remember that one byte contains eight bits for double precision variables), you can use simple use the **itemsize** functionality (the array $x$ is actually an object which inherits the functionalities defined in Numpy) as"
    ]
@@ -1075,17 +1139,12 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "4257fe2f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "8\n"
-     ]
-    }
-   ],
+   "id": "31ea96f7",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "x = np.log(np.array([4.0, 7.0, 8.0]))\n",
@@ -1094,8 +1153,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "00e47a6c",
-   "metadata": {},
+   "id": "fafcd586",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Matrices in Python\n",
     "\n",
@@ -1107,19 +1168,12 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "d65874c6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1.38629436 1.94591015 2.07944154]\n",
-      " [1.09861229 2.30258509 2.39789527]\n",
-      " [1.38629436 1.60943791 1.94591015]]\n"
-     ]
-    }
-   ],
+   "id": "89a4e163",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))\n",
@@ -1128,8 +1182,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d35d218b",
-   "metadata": {},
+   "id": "d2fd33a8",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "If we use the **shape** function we would get $(3, 3)$ as output, that is verifying that our matrix is a $3\\times 3$ matrix. We can slice the matrix and print for example the first column (Python organized matrix elements in a row-major order, see below) as"
    ]
@@ -1137,17 +1193,12 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "e92e4468",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1.38629436 1.09861229 1.38629436]\n"
-     ]
-    }
-   ],
+   "id": "78dc0dd8",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))\n",
@@ -1157,8 +1208,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1bfc673c",
-   "metadata": {},
+   "id": "f4e7f11f",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "We can continue this was by printing out other columns or rows. The example here prints out the second column"
    ]
@@ -1166,17 +1219,12 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "534fd820",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1.09861229 2.30258509 2.39789527]\n"
-     ]
-    }
-   ],
+   "id": "6dbdb019",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))\n",
@@ -1186,8 +1234,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8002cf95",
-   "metadata": {},
+   "id": "17436a55",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "Numpy contains many other functionalities that allow us to slice, subdivide etc etc arrays. We strongly recommend that you look up the [Numpy website for more details](http://www.numpy.org/). Useful functions when defining a matrix are the **np.zeros** function which declares a matrix of a given dimension and sets all elements to zero"
    ]
@@ -1195,26 +1245,12 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "8ecff438",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
-      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
-      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
-      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
-      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
-      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
-      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
-      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
-      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
-      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n"
-     ]
-    }
-   ],
+   "id": "3cf6daef",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "n = 10\n",
@@ -1225,8 +1261,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b02eb5ec",
-   "metadata": {},
+   "id": "a9429c1c",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "or initializing all elements to"
    ]
@@ -1234,26 +1272,12 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "a0725d66",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
-      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
-      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
-      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
-      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
-      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
-      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
-      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
-      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
-      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]\n"
-     ]
-    }
-   ],
+   "id": "4f69eccb",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "n = 10\n",
@@ -1264,8 +1288,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c0358873",
-   "metadata": {},
+   "id": "0dbbf649",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "or as unitarily distributed random numbers (see the material on random number generators in the statistics part)"
    ]
@@ -1273,36 +1299,12 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "ffa25ac1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[0.11833235 0.97168087 0.51108098 0.55183233 0.25342454 0.06807213\n",
-      "  0.57524476 0.19248643 0.41798071 0.07388678]\n",
-      " [0.41086978 0.75202815 0.33196421 0.16708428 0.16608737 0.29586216\n",
-      "  0.09944873 0.05277619 0.47712039 0.51731953]\n",
-      " [0.71338175 0.41548522 0.65654583 0.64709545 0.35227217 0.88797877\n",
-      "  0.5737164  0.81603041 0.31729969 0.82999556]\n",
-      " [0.47037752 0.92176294 0.2823057  0.07829819 0.65495048 0.36686303\n",
-      "  0.27656187 0.60957578 0.69888079 0.668314  ]\n",
-      " [0.24924292 0.21878186 0.40947806 0.19907785 0.9566047  0.52848094\n",
-      "  0.25434325 0.59920581 0.39336845 0.23717584]\n",
-      " [0.59463514 0.15383958 0.65652548 0.45028046 0.16413801 0.88895409\n",
-      "  0.2719153  0.93751885 0.51389771 0.61062516]\n",
-      " [0.88609214 0.93589998 0.60777444 0.10998959 0.60805073 0.52485563\n",
-      "  0.57517889 0.89619395 0.65701361 0.83664686]\n",
-      " [0.93536673 0.668859   0.03717754 0.47723192 0.13950965 0.75354895\n",
-      "  0.66283949 0.10722441 0.0417946  0.24890667]\n",
-      " [0.25666182 0.88349025 0.73006765 0.30795969 0.86898117 0.55923736\n",
-      "  0.35818128 0.87468503 0.11364813 0.86332561]\n",
-      " [0.09205753 0.36857883 0.34677484 0.45563071 0.47895131 0.7425919\n",
-      "  0.6911375  0.49646786 0.65869839 0.29348232]]\n"
-     ]
-    }
-   ],
+   "id": "0a760b65",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "n = 10\n",
@@ -1313,8 +1315,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1b84b108",
-   "metadata": {},
+   "id": "72466600",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "As we will see throughout these lectures, there are several extremely useful functionalities in Numpy.\n",
     "As an example, consider the discussion of the covariance matrix. Suppose we have defined three vectors\n",
@@ -1323,8 +1327,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "069d4525",
-   "metadata": {},
+   "id": "566c7175",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\boldsymbol{\\Sigma} = \\begin{bmatrix} \\sigma_{xx} & \\sigma_{xy} & \\sigma_{xz} \\\\\n",
@@ -1336,16 +1342,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "952305ae",
-   "metadata": {},
+   "id": "e2ac755d",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "where for example"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fb6a6cdd",
-   "metadata": {},
+   "id": "4df1d8a2",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\sigma_{xy} =\\frac{1}{n} \\sum_{i=0}^{n-1}(x_i- \\overline{x})(y_i- \\overline{y}).\n",
@@ -1354,8 +1364,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "adc20a95",
-   "metadata": {},
+   "id": "f3004075",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "The Numpy function **np.cov** calculates the covariance elements using the factor $1/(n-1)$ instead of $1/n$ since it assumes we do not have the exact mean values. \n",
     "The following simple function uses the **np.vstack** function which takes each vector of dimension $1\\times n$ and produces a $3\\times n$ matrix $\\boldsymbol{W}$"
@@ -1363,8 +1375,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b2a37a12",
-   "metadata": {},
+   "id": "97be6b4e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\boldsymbol{W} = \\begin{bmatrix} x_0 & x_1 & x_2 & \\dots & x_{n-2} & x_{n-1} \\\\\n",
@@ -1376,8 +1390,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d2db2071",
-   "metadata": {},
+   "id": "bae55e18",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "which in turn is converted into into the $3\\times 3$ covariance matrix\n",
     "$\\boldsymbol{\\Sigma}$ via the Numpy function **np.cov()**. We note that we can also calculate\n",
@@ -1389,23 +1405,12 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "a9fc9d7e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-0.016702121542687232\n",
-      "3.9180630130818526\n",
-      "0.051686343335408516\n",
-      "[[ 1.0747946   3.28558848  3.20209898]\n",
-      " [ 3.28558848 10.9851108   9.5799141 ]\n",
-      " [ 3.20209898  9.5799141  15.11959546]]\n",
-      "[23.76779023  0.07048992  3.34122072]\n"
-     ]
-    }
-   ],
+   "id": "a19dcdb6",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "# Importing various packages\n",
     "import numpy as np\n",
@@ -1427,34 +1432,12 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "9c6bf314",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1. 0. 0. 0.]\n",
-      " [0. 1. 0. 0.]\n",
-      " [0. 0. 1. 0.]\n",
-      " [0. 0. 0. 1.]]\n",
-      "  (0, 0)\t1.0\n",
-      "  (1, 1)\t1.0\n",
-      "  (2, 2)\t1.0\n",
-      "  (3, 3)\t1.0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "id": "09008c51",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "\n",
@@ -1473,8 +1456,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "26ae1a84",
-   "metadata": {},
+   "id": "0e4d77ce",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Meet the Pandas\n",
     "\n",
@@ -1497,81 +1482,12 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "4cfb6b53",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>First Name</th>\n",
-       "      <th>Last Name</th>\n",
-       "      <th>Place of birth</th>\n",
-       "      <th>Date of Birth T.A.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Frodo</td>\n",
-       "      <td>Baggins</td>\n",
-       "      <td>Shire</td>\n",
-       "      <td>2968</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Bilbo</td>\n",
-       "      <td>Baggins</td>\n",
-       "      <td>Shire</td>\n",
-       "      <td>2890</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Aragorn II</td>\n",
-       "      <td>Elessar</td>\n",
-       "      <td>Eriador</td>\n",
-       "      <td>2931</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Samwise</td>\n",
-       "      <td>Gamgee</td>\n",
-       "      <td>Shire</td>\n",
-       "      <td>2980</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   First Name Last Name Place of birth  Date of Birth T.A.\n",
-       "0       Frodo   Baggins          Shire                2968\n",
-       "1       Bilbo   Baggins          Shire                2890\n",
-       "2  Aragorn II   Elessar        Eriador                2931\n",
-       "3     Samwise    Gamgee          Shire                2980"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "id": "b16f70b8",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "from IPython.display import display\n",
@@ -1586,8 +1502,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "542ee6e0",
-   "metadata": {},
+   "id": "d9ffe7d4",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "In the above we have imported **pandas** with the shorthand **pd**, the latter has become the standard way we import **pandas**. We make then a list of various variables\n",
     "and reorganize the aboves lists into a **DataFrame** and then print out  a neat table with specific column labels as *Name*, *place of birth* and *date of birth*.\n",
@@ -1598,81 +1516,12 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "61a0f2fb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>First Name</th>\n",
-       "      <th>Last Name</th>\n",
-       "      <th>Place of birth</th>\n",
-       "      <th>Date of Birth T.A.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Frodo</th>\n",
-       "      <td>Frodo</td>\n",
-       "      <td>Baggins</td>\n",
-       "      <td>Shire</td>\n",
-       "      <td>2968</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Bilbo</th>\n",
-       "      <td>Bilbo</td>\n",
-       "      <td>Baggins</td>\n",
-       "      <td>Shire</td>\n",
-       "      <td>2890</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Aragorn</th>\n",
-       "      <td>Aragorn II</td>\n",
-       "      <td>Elessar</td>\n",
-       "      <td>Eriador</td>\n",
-       "      <td>2931</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Sam</th>\n",
-       "      <td>Samwise</td>\n",
-       "      <td>Gamgee</td>\n",
-       "      <td>Shire</td>\n",
-       "      <td>2980</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         First Name Last Name Place of birth  Date of Birth T.A.\n",
-       "Frodo         Frodo   Baggins          Shire                2968\n",
-       "Bilbo         Bilbo   Baggins          Shire                2890\n",
-       "Aragorn  Aragorn II   Elessar        Eriador                2931\n",
-       "Sam         Samwise    Gamgee          Shire                2980"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "id": "e83c940e",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "data_pandas = pd.DataFrame(data,index=['Frodo','Bilbo','Aragorn','Sam'])\n",
     "display(data_pandas)"
@@ -1680,8 +1529,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c4603418",
-   "metadata": {},
+   "id": "adc18bb0",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "Thereafter we display the content of the row which begins with the index **Aragorn**"
    ]
@@ -1689,31 +1540,22 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "311a5e3e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "First Name            Aragorn II\n",
-       "Last Name                Elessar\n",
-       "Place of birth           Eriador\n",
-       "Date of Birth T.A.          2931\n",
-       "Name: Aragorn, dtype: object"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "id": "e0d15cde",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "display(data_pandas.loc['Aragorn'])"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b3cbb735",
-   "metadata": {},
+   "id": "aae31c67",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "We can easily append data to this, for example"
    ]
@@ -1721,22 +1563,12 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "9f4ea5fc",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'DataFrame' object has no attribute 'append'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_27009/1326197715.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m new_hobbit = {'First Name': [\"Peregrin\"],\n\u001b[0m\u001b[1;32m      7\u001b[0m               \u001b[0;34m'Last Name'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Took\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m               \u001b[0;34m'Place of birth'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Shire\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m               \u001b[0;34m'Date of Birth T.A.'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2990\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   6200\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6201\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6202\u001b[0m         ):\n\u001b[1;32m   6203\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6204\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'append'"
-     ]
-    }
-   ],
+   "id": "c105a1d3",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "new_hobbit = {'First Name': [\"Peregrin\"],\n",
     "              'Last Name': [\"Took\"],\n",
@@ -1749,8 +1581,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "97abc234",
-   "metadata": {},
+   "id": "0a66a649",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "Here are other examples where we use the **DataFrame** functionality to handle arrays, now with more interesting features for us, namely numbers. We set up a matrix \n",
     "of dimensionality $10\\times 5$ and compute the mean value and standard deviation of each column. Similarly, we can perform mathematial operations like squaring the matrix elements and many other operations."
@@ -1758,9 +1592,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "24324844",
-   "metadata": {},
+   "execution_count": 21,
+   "id": "8fafcc6c",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -1780,17 +1617,22 @@
   },
   {
    "cell_type": "markdown",
-   "id": "43df7996",
-   "metadata": {},
+   "id": "ce59ce6c",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "Thereafter we can select specific columns only and plot final results"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "c47e2a3e",
-   "metadata": {},
+   "execution_count": 22,
+   "id": "37be9b68",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "df.columns = ['First', 'Second', 'Third', 'Fourth', 'Fifth']\n",
@@ -1815,17 +1657,22 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e437012d",
-   "metadata": {},
+   "id": "e603c881",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "We can produce a $4\\times 4$ matrix"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "40a9171b",
-   "metadata": {},
+   "execution_count": 23,
+   "id": "66caa708",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "b = np.arange(16).reshape((4,4))\n",
@@ -1836,8 +1683,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "07d049ee",
-   "metadata": {},
+   "id": "d78f30ea",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "and many other operations. \n",
     "\n",
@@ -1851,8 +1700,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9efbc60f",
-   "metadata": {},
+   "id": "d5c358cc",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "### Simple linear regression model using **scikit-learn**\n",
     "\n",
@@ -1880,8 +1731,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ad0bf311",
-   "metadata": {},
+   "id": "8a1bf1cf",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "y = 2x+N(0,1),\n",
@@ -1890,8 +1743,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "73ada8c0",
-   "metadata": {},
+   "id": "a058d050",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "where $N(0,1)$ represents random numbers generated by the normal\n",
     "distribution.  From **Scikit-Learn** we import then the\n",
@@ -1915,9 +1770,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "e4aeced9",
-   "metadata": {},
+   "execution_count": 24,
+   "id": "229b6771",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "# Importing various packages\n",
@@ -1943,8 +1801,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9dfc99f0",
-   "metadata": {},
+   "id": "d60ff0fb",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "This example serves several aims. It allows us to demonstrate several\n",
     "aspects of data analysis and later machine learning algorithms. The\n",
@@ -1958,8 +1818,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "428eb424",
-   "metadata": {},
+   "id": "9edaf7a2",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "y = 10x+0.01 \\times N(0,1),\n",
@@ -1968,8 +1830,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5b5a0c6e",
-   "metadata": {},
+   "id": "7239a885",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "where $x$ is defined as before.  Does the fit look better? Indeed, by\n",
     "reducing the role of the noise given by the normal distribution we see immediately that\n",
@@ -1987,8 +1851,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f0fa3206",
-   "metadata": {},
+   "id": "b3104242",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\chi^2 = \\frac{1}{n}\n",
@@ -1998,8 +1864,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e7b223c5",
-   "metadata": {},
+   "id": "c3596efa",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "where $\\sigma_i^2$ is the variance (to be defined later) of the entry\n",
     "$y_i$.  We may not know the explicit value of $\\sigma_i^2$, it serves\n",
@@ -2027,8 +1895,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e628fb6a",
-   "metadata": {},
+   "id": "1ff68247",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\epsilon_{\\mathrm{relative}}= \\frac{\\vert \\boldsymbol{y} -\\boldsymbol{\\tilde{y}}\\vert}{\\vert \\boldsymbol{y}\\vert}.\n",
@@ -2037,8 +1907,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8263fca7",
-   "metadata": {},
+   "id": "c3286c28",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "The squared cost function results in an arithmetic mean-unbiased\n",
     "estimator, and the absolute-value cost function results in a\n",
@@ -2052,9 +1924,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "8b269629",
-   "metadata": {},
+   "execution_count": 25,
+   "id": "3592db45",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -2077,8 +1952,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c68f4716",
-   "metadata": {},
+   "id": "fac28c8e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "Depending on the parameter in front of the normal distribution, we may\n",
     "have a small or larger relative error. Try to play around with\n",
@@ -2096,9 +1973,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "b9536a0a",
-   "metadata": {},
+   "execution_count": 26,
+   "id": "0968aade",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "import numpy as np \n",
@@ -2132,8 +2012,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "545ce818",
-   "metadata": {},
+   "id": "2af007c2",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "The function **coef** gives us the parameter $\\beta$ of our fit while **intercept** yields \n",
     "$\\alpha$. Depending on the constant in front of the normal distribution, we get values near or far from $\\alpha =2$ and $\\beta =5$. Try to play around with different parameters in front of the normal distribution. The function **meansquarederror** gives us the mean square error, a risk metric corresponding to the expected value of the squared (quadratic) error or loss defined as"
@@ -2141,8 +2023,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "90848339",
-   "metadata": {},
+   "id": "6af4ecb9",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "MSE(\\boldsymbol{y},\\boldsymbol{\\tilde{y}}) = \\frac{1}{n}\n",
@@ -2152,8 +2036,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c740c99d",
-   "metadata": {},
+   "id": "7c8dbf8d",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "The smaller the value, the better the fit. Ideally we would like to\n",
     "have an MSE equal zero.  The attentive reader has probably recognized\n",
@@ -2171,8 +2057,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "deff6a8b",
-   "metadata": {},
+   "id": "869a78db",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "R^2(\\boldsymbol{y}, \\tilde{\\boldsymbol{y}}) = 1 - \\frac{\\sum_{i=0}^{n - 1} (y_i - \\tilde{y}_i)^2}{\\sum_{i=0}^{n - 1} (y_i - \\bar{y})^2},\n",
@@ -2181,16 +2069,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "69d9fe36",
-   "metadata": {},
+   "id": "d12c7566",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "where we have defined the mean value  of $\\boldsymbol{y}$ as"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "054d0dd1",
-   "metadata": {},
+   "id": "5d00656e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\bar{y} =  \\frac{1}{n} \\sum_{i=0}^{n - 1} y_i.\n",
@@ -2199,8 +2091,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ed027b67",
-   "metadata": {},
+   "id": "720e743e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "Another quantity taht we will meet again in our discussions of regression analysis is \n",
     " the mean absolute error (MAE), a risk metric corresponding to the expected value of the absolute error loss or what we call the $l1$-norm loss. In our discussion above we presented the relative error.\n",
@@ -2209,8 +2103,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a741491c",
-   "metadata": {},
+   "id": "b96282db",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\text{MAE}(\\boldsymbol{y}, \\boldsymbol{\\tilde{y}}) = \\frac{1}{n} \\sum_{i=0}^{n-1} \\left| y_i - \\tilde{y}_i \\right|.\n",
@@ -2219,8 +2115,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1528b10c",
-   "metadata": {},
+   "id": "85d7da19",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "We present the \n",
     "squared logarithmic (quadratic) error"
@@ -2228,8 +2126,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2324aa18",
-   "metadata": {},
+   "id": "eb046bf3",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\text{MSLE}(\\boldsymbol{y}, \\boldsymbol{\\tilde{y}}) = \\frac{1}{n} \\sum_{i=0}^{n - 1} (\\log_e (1 + y_i) - \\log_e (1 + \\tilde{y}_i) )^2,\n",
@@ -2238,8 +2138,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5c819d3b",
-   "metadata": {},
+   "id": "4012ae06",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "where $\\log_e (x)$ stands for the natural logarithm of $x$. This error\n",
     "estimate is best to use when targets having exponential growth, such\n",
@@ -2258,8 +2160,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f692938",
-   "metadata": {},
+   "id": "a22b912f",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "H_{\\delta}(\\boldsymbol{a})=\\left\\{\\begin{array}{cc}\\frac{1}{2} \\boldsymbol{a}^{2}& \\text{for }|\\boldsymbol{a}|\\leq \\delta\\\\ \\delta (|\\boldsymbol{a}|-\\frac{1}{2}\\delta ),&\\text{otherwise}.\\end{array}\\right.\n",
@@ -2268,8 +2172,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0118548c",
-   "metadata": {},
+   "id": "9b714af5",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "Here $\\boldsymbol{a}=\\boldsymbol{y} - \\boldsymbol{\\tilde{y}}$.\n",
     "\n",
@@ -2279,8 +2185,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6a77f30f",
-   "metadata": {},
+   "id": "d3ddca8f",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "### To our real data: nuclear binding energies. Brief reminder on masses and binding energies\n",
     "\n",
@@ -2294,8 +2202,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9e5e0f07",
-   "metadata": {},
+   "id": "a35c9c04",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\Delta M(N, Z) =  M(N, Z) - uA,\n",
@@ -2304,16 +2214,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bbcb4130",
-   "metadata": {},
+   "id": "62853d17",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "where $u$ is the Atomic Mass Unit"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a8b137e1",
-   "metadata": {},
+   "id": "e5ad3f52",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "u = M(^{12}\\mathrm{C})/12 = 931.4940954(57) \\hspace{0.1cm} \\mathrm{MeV}/c^2.\n",
@@ -2322,16 +2236,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f88c848c",
-   "metadata": {},
+   "id": "f190d538",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "The nucleon masses are"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6547c4ce",
-   "metadata": {},
+   "id": "7ba97b10",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "m_p =  1.00727646693(9)u,\n",
@@ -2340,16 +2258,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5fa8bc43",
-   "metadata": {},
+   "id": "144c28e2",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "and"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3570dae8",
-   "metadata": {},
+   "id": "cb5abb62",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "m_n = 939.56536(8)\\hspace{0.1cm} \\mathrm{MeV}/c^2 = 1.0086649156(6)u.\n",
@@ -2358,8 +2280,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2903e3c8",
-   "metadata": {},
+   "id": "b847a061",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "In the [2016 mass evaluation of by W.J.Huang, G.Audi, M.Wang, F.G.Kondev, S.Naimi and X.Xu](http://nuclearmasses.org/resources_folder/Wang_2017_Chinese_Phys_C_41_030003.pdf)\n",
     "there are data on masses and decays of 3437 nuclei.\n",
@@ -2372,8 +2296,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dce5fba5",
-   "metadata": {},
+   "id": "063e2b1a",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "BE(N, Z) = ZM_H c^2 + Nm_n c^2 - M(N, Z)c^2 ,\n",
@@ -2382,8 +2308,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7f49ea93",
-   "metadata": {},
+   "id": "e9f4cc78",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "where $M_H$ is the mass of the hydrogen atom and $m_n$ is the mass of the neutron.\n",
     "In terms of the mass excess the binding energy is given by"
@@ -2391,8 +2319,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b229130c",
-   "metadata": {},
+   "id": "a5325218",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "BE(N, Z) = Z\\Delta_H c^2 + N\\Delta_n c^2 -\\Delta(N, Z)c^2 ,\n",
@@ -2401,8 +2331,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "00a8181b",
-   "metadata": {},
+   "id": "48ff0610",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "where $\\Delta_H c^2 = 7.2890$ MeV and $\\Delta_n c^2 = 8.0713$ MeV.\n",
     "\n",
@@ -2413,8 +2345,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ed3e9886",
-   "metadata": {},
+   "id": "20f8f640",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "BE(N,Z) = a_1A-a_2A^{2/3}-a_3\\frac{Z^2}{A^{1/3}}-a_4\\frac{(N-Z)^2}{A},\n",
@@ -2423,8 +2357,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9833ea6e",
-   "metadata": {},
+   "id": "31ee60c0",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "where $A$ stands for the number of nucleons and the $a_i$s are parameters which are determined by a fit \n",
     "to the experimental data.  \n",
@@ -2446,8 +2382,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e586b4cd",
-   "metadata": {},
+   "id": "080bcec2",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "### Organizing our data\n",
     "\n",
@@ -2460,9 +2398,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "5b86a2f2",
-   "metadata": {},
+   "execution_count": 27,
+   "id": "b541d397",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "# Common imports\n",
@@ -2502,31 +2443,24 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6d8999da",
-   "metadata": {},
+   "id": "fbc01a07",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "Before we proceed, we define also a function for making our plots. You can obviously avoid this and simply set up various **matplotlib** commands every time you need them. You may however find it convenient to collect all such commands in one function and simply call this function."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "ca868b5c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\"\\nfrom pylab import plt, mpl\\nplt.style.use(\\'seaborn\\')\\nmpl.rcParams[\\'font.family\\'] = \\'serif\\'\\n\\ndef MakePlot(x,y, styles, labels, axlabels):\\n    plt.figure(figsize=(10,6))\\n    for i in range(len(x)):\\n        plt.plot(x[i], y[i], styles[i], label = labels[i])\\n        plt.xlabel(axlabels[0])\\n        plt.ylabel(axlabels[1])\\n    plt.legend(loc=0)\\n'"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": 28,
+   "id": "d99f193a",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
-    "\"\"\"\"\n",
     "from pylab import plt, mpl\n",
     "plt.style.use('seaborn')\n",
     "mpl.rcParams['font.family'] = 'serif'\n",
@@ -2537,14 +2471,15 @@
     "        plt.plot(x[i], y[i], styles[i], label = labels[i])\n",
     "        plt.xlabel(axlabels[0])\n",
     "        plt.ylabel(axlabels[1])\n",
-    "    plt.legend(loc=0)\n",
-    "\"\"\"    "
+    "    plt.legend(loc=0)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "265fc848",
-   "metadata": {},
+   "id": "cf8ec78d",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "Our next step is to read the data on experimental binding energies and\n",
     "reorganize them as functions of the mass number $A$, the number of\n",
@@ -2557,21 +2492,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "edef3728",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'                                                                                                                         \\nThis is taken from the data file of the mass 2016 evaluation.                                                               \\nAll files are 3436 lines long with 124 character per line.                                                                  \\n       Headers are 39 lines long.                                                                                           \\n   col 1     :  Fortran character control: 1 = page feed  0 = line feed                                                     \\n   format    :  a1,i3,i5,i5,i5,1x,a3,a4,1x,f13.5,f11.5,f11.3,f9.3,1x,a2,f11.3,f9.3,1x,i3,1x,f12.5,f11.5                     \\n   These formats are reflected in the pandas widths variable below, see the statement                                       \\n   widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1),                                                            \\n   Pandas has also a variable header, with length 39 in this case.                                                          \\n'"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": 29,
+   "id": "015fa1ac",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "\"\"\"                                                                                                                         \n",
     "This is taken from the data file of the mass 2016 evaluation.                                                               \n",
@@ -2587,8 +2514,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dd0034af",
-   "metadata": {},
+   "id": "7d3eae6c",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "The data we are interested in are in columns 2, 3, 4 and 11, giving us\n",
     "the number of neutrons, protons, mass numbers and binding energies,\n",
@@ -2598,9 +2527,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "0a2099c5",
-   "metadata": {},
+   "execution_count": 30,
+   "id": "5c526bbb",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "# Read the experimental data with Pandas\n",
@@ -2625,8 +2557,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6d64a9c6",
-   "metadata": {},
+   "id": "df39cc1c",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "We have now read in the data, grouped them according to the variables we are interested in. \n",
     "We see how easy it is to reorganize the data using **pandas**. If we\n",
@@ -2642,32 +2576,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "c36ad284",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "            N    Z    A Element  Ebinding\n",
-      "A                                        \n",
-      "4   0       1    3    4      Li  1.153760\n",
-      "5   2       3    2    5      He  5.512132\n",
-      "6   7       3    3    6      Li  5.332331\n",
-      "7   12      4    3    7      Li  5.606439\n",
-      "8   17      4    4    8      Be  7.062435\n",
-      "...       ...  ...  ...     ...       ...\n",
-      "264 3297  156  108  264      Hs  7.298375\n",
-      "265 3303  157  108  265      Hs  7.296247\n",
-      "266 3310  158  108  266      Hs  7.298273\n",
-      "269 3331  159  110  269      Ds  7.250154\n",
-      "270 3337  160  110  270      Ds  7.253775\n",
-      "\n",
-      "[264 rows x 5 columns]\n"
-     ]
-    }
-   ],
+   "execution_count": 31,
+   "id": "f9f23012",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "A = Masses['A']\n",
     "Z = Masses['Z']\n",
@@ -2679,8 +2594,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b18a2c0",
-   "metadata": {},
+   "id": "6bac4dfd",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "The next step, and we will define this mathematically later, is to set up the so-called **design matrix**. We will throughout call this matrix $\\boldsymbol{X}$.\n",
     "It has dimensionality $p\\times n$, where $n$ is the number of data points and $p$ are the so-called predictors. In our case here they are given by the number of polynomials in $A$ we wish to include in the fit."
@@ -2688,9 +2605,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "id": "1ad1fff9",
-   "metadata": {},
+   "execution_count": 32,
+   "id": "75c7edbc",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "# Now we set up the design matrix X\n",
@@ -2704,17 +2624,22 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8418a2aa",
-   "metadata": {},
+   "id": "2bde2373",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "With **scikitlearn** we are now ready to use linear regression and fit our data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "id": "1ea1c99f",
-   "metadata": {},
+   "execution_count": 33,
+   "id": "531b66ec",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "clf = skl.LinearRegression().fit(X, Energies)\n",
@@ -2723,8 +2648,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d184b5df",
-   "metadata": {},
+   "id": "8e68e855",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "Pretty simple!  \n",
     "Now we can print measures of how our fit is doing, the coefficients from the fits and plot the final fit together with our data."
@@ -2732,36 +2659,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "id": "4bb96bf8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean squared error: 0.02\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 34,
+   "id": "8fd0da14",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "# The mean squared error                               \n",
     "print(\"Mean squared error: %.2f\" % mean_squared_error(Energies, fity))\n",
-    "## Explained variance score: 1 is perfect prediction                                 \n",
-    "#print('Variance score: %.2f' % r2_score(Energies, fity))\n",
+    "# Explained variance score: 1 is perfect prediction                                 \n",
+    "print('Variance score: %.2f' % r2_score(Energies, fity))\n",
     "# Mean absolute error                                                           \n",
-    "#print('Mean absolute error: %.2f' % mean_absolute_error(Energies, fity))\n",
-    "#print(clf.coef_, clf.intercept_)\n",
+    "print('Mean absolute error: %.2f' % mean_absolute_error(Energies, fity))\n",
+    "print(clf.coef_, clf.intercept_)\n",
     "\n",
     "Masses['Eapprox']  = fity\n",
     "# Generate a plot comparing the experimental with the fitted values values.\n",
@@ -2779,8 +2691,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a6ca4c82",
-   "metadata": {},
+   "id": "d5f64820",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "### And what about using neural networks?\n",
     "\n",
@@ -2790,93 +2704,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "id": "c3072d47",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[2.45926087e+02 9.09631786e+02 5.49989401e+02 6.44682919e+02\n",
-      "  1.00152026e+03 2.98229636e+02]\n",
-      " [1.60487177e+01 1.11327058e+01 1.89892178e+01 2.91355329e+01\n",
-      "  1.84191515e+03 1.91127240e+01]\n",
-      " [5.15218523e+00 1.33283018e+01 2.01038393e+01 1.32049628e+00\n",
-      "  4.70650883e+00 1.38520020e+01]\n",
-      " [2.05834021e-01 1.33912893e-01 2.11723352e-01 2.47339004e-01\n",
-      "  6.80883632e-02 3.49468006e-01]\n",
-      " [1.26217089e-01 1.70682500e-01 1.56794335e-01 9.39134042e-01\n",
-      "  1.64027461e+01 2.59164091e-01]\n",
-      " [1.66844981e+02 8.39153402e+01 1.70546784e+02 4.85399838e+00\n",
-      "  1.74079354e+01 1.25607419e+02]]\n"
-     ]
-    }
-   ],
+   "execution_count": 35,
+   "id": "95a48f39",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "from sklearn.neural_network import MLPRegressor\n",
     "from sklearn.metrics import accuracy_score\n",
@@ -2888,8 +2722,8 @@
     "n_hidden_neurons = 50\n",
     "epochs = 100\n",
     "# store models for later use\n",
-    "eta_vals = np.logspace(-5, 0, 6)\n",
-    "lmbd_vals = np.logspace(-5, 0, 6)\n",
+    "eta_vals = np.logspace(-3, 0, 4)\n",
+    "lmbd_vals = np.logspace(-3, 0, 4)\n",
     "# store the models for later use\n",
     "DNN_scikit = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n",
     "train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))\n",
@@ -2903,7 +2737,7 @@
     "        train_accuracy[i][j] = dnn.score(X_train, Y_train)\n",
     "        fity = dnn.predict(X_train)\n",
     "        MSE = mean_squared_error(Y_train, fity)\n",
-    "#        print(\"Mean squared error: %.2f\" % mean_squared_error(Y_train, fity))\n",
+    "        print(\"Mean squared error: %.2f\" % mean_squared_error(Y_train, fity))\n",
     "        train_accuracy[i][j] = MSE\n",
     "fig, ax = plt.subplots(figsize = (10, 10))\n",
     "sns.heatmap(train_accuracy, annot=True, ax=ax, cmap=\"viridis\")\n",
@@ -2916,8 +2750,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ded40485",
-   "metadata": {},
+   "id": "5f591abe",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## A first summary\n",
     "\n",
@@ -2934,8 +2770,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bf969507",
-   "metadata": {},
+   "id": "68ccfdd7",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Why Linear Regression (aka Ordinary Least Squares and family)\n",
     "\n",
@@ -2964,8 +2802,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b1b95ecd",
-   "metadata": {},
+   "id": "e832a937",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Regression analysis, overarching aims\n",
     "\n",
@@ -2984,8 +2824,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "86523480",
-   "metadata": {},
+   "id": "1d52791a",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Regression analysis, overarching aims II\n",
     "\n",
@@ -3010,8 +2852,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "13322e35",
-   "metadata": {},
+   "id": "f3608eb8",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Examples\n",
     "In order to understand the relation among the predictors (or features or properties) $p$, the set of data $n$ and the target (outcome, output etc) $\\boldsymbol{y}$,\n",
@@ -3023,8 +2867,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0078a071",
-   "metadata": {},
+   "id": "c0c96130",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "BE(A) = a_0+a_1A+a_2A^{2/3}+a_3A^{-1/3}+a_4A^{-1},\n",
@@ -3033,8 +2879,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "79bdef1f",
-   "metadata": {},
+   "id": "c6214ecc",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "we have five predictors, that is the intercept, the $A$ dependent term, the $A^{2/3}$ term and the $A^{-1/3}$ and $A^{-1}$ terms.\n",
     "This gives $p=0,1,2,3,4$. Furthermore we have $n$ entries for each predictor. It means that our design matrix is a \n",
@@ -3046,8 +2894,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "151d70ab",
-   "metadata": {},
+   "id": "d3429900",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## General linear models and linear algebra\n",
     "Before we proceed let us study a case where we aim at fitting a set of data $\\boldsymbol{y}=[y_0,y_1,\\dots,y_{n-1}]$. We could think of these data as a result of an experiment or a complicated numerical experiment. These data are functions of a series of variables $\\boldsymbol{x}=[x_0,x_1,\\dots,x_{n-1}]$, that is $y_i = y(x_i)$ with $i=0,1,2,\\dots,n-1$. The variables $x_i$ could represent physical quantities like time, temperature, position etc. We assume that $y(x)$ is a smooth function. \n",
@@ -3057,8 +2907,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d60946c5",
-   "metadata": {},
+   "id": "bf83bf87",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "y=y(x) \\rightarrow y(x_i)=\\tilde{y}_i+\\epsilon_i=\\sum_{j=0}^{n-1} \\beta_j x_i^j+\\epsilon_i,\n",
@@ -3067,16 +2919,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b5921125",
-   "metadata": {},
+   "id": "8235e0ac",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "where $\\epsilon_i$ is the error in our approximation."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "01a6db48",
-   "metadata": {},
+   "id": "64a57e7b",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Rewriting the fitting procedure as a linear algebra problem\n",
     "For every set of values $y_i,x_i$ we have thus the corresponding set of equations"
@@ -3084,8 +2940,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "910c9857",
-   "metadata": {},
+   "id": "1261c0a6",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\begin{align*}\n",
@@ -3100,8 +2958,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bb4926b2",
-   "metadata": {},
+   "id": "4a2daa1f",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Rewriting the fitting procedure as a linear algebra problem, more details\n",
     "Defining the vectors"
@@ -3109,8 +2969,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16d82720",
-   "metadata": {},
+   "id": "707fc069",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\boldsymbol{y} = [y_0,y_1, y_2,\\dots, y_{n-1}]^T,\n",
@@ -3119,16 +2981,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1985845d",
-   "metadata": {},
+   "id": "82702653",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "and"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "034b0a2f",
-   "metadata": {},
+   "id": "8f52f6c1",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\boldsymbol{\\beta} = [\\beta_0,\\beta_1, \\beta_2,\\dots, \\beta_{n-1}]^T,\n",
@@ -3137,16 +3003,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "79acfd67",
-   "metadata": {},
+   "id": "7ffc95af",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "and"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6268dff7",
-   "metadata": {},
+   "id": "df6512ce",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\boldsymbol{\\epsilon} = [\\epsilon_0,\\epsilon_1, \\epsilon_2,\\dots, \\epsilon_{n-1}]^T,\n",
@@ -3155,16 +3025,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5dfd0735",
-   "metadata": {},
+   "id": "d181ad09",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "and the design matrix"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6fffafff",
-   "metadata": {},
+   "id": "d0539302",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\boldsymbol{X}=\n",
@@ -3180,16 +3054,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3fb49db2",
-   "metadata": {},
+   "id": "a547c282",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "we can rewrite our equations as"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "86bc3042",
-   "metadata": {},
+   "id": "2459d432",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\boldsymbol{y} = \\boldsymbol{X}\\boldsymbol{\\beta}+\\boldsymbol{\\epsilon}.\n",
@@ -3198,16 +3076,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7257a7db",
-   "metadata": {},
+   "id": "4610abbb",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "The above design matrix is called a [Vandermonde matrix](https://en.wikipedia.org/wiki/Vandermonde_matrix)."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7ad41796",
-   "metadata": {},
+   "id": "aef4e26e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Generalizing the fitting procedure as a linear algebra problem\n",
     "\n",
@@ -3220,8 +3102,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1f969e57",
-   "metadata": {},
+   "id": "70d7a09e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\begin{align*}\n",
@@ -3238,16 +3122,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "26a5823e",
-   "metadata": {},
+   "id": "dc095627",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "**Note that we have $p=n$ here. The matrix is symmetric. This is generally not the case!**"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ca8996bd",
-   "metadata": {},
+   "id": "d0fac735",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Generalizing the fitting procedure as a linear algebra problem\n",
     "We redefine in turn the matrix $\\boldsymbol{X}$ as"
@@ -3255,8 +3143,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "94a29694",
-   "metadata": {},
+   "id": "a9ca16da",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\boldsymbol{X}=\n",
@@ -3272,16 +3162,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b37bb44",
-   "metadata": {},
+   "id": "0a9a60d0",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "and without loss of generality we rewrite again  our equations as"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c3fcf170",
-   "metadata": {},
+   "id": "3c143d3d",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\boldsymbol{y} = \\boldsymbol{X}\\boldsymbol{\\beta}+\\boldsymbol{\\epsilon}.\n",
@@ -3290,16 +3184,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "31791045",
-   "metadata": {},
+   "id": "fd2088d4",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "The left-hand side of this equation is kwown. Our error vector $\\boldsymbol{\\epsilon}$ and the parameter vector $\\boldsymbol{\\beta}$ are our unknow quantities. How can we obtain the optimal set of $\\beta_i$ values?"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "212f90a6",
-   "metadata": {},
+   "id": "09bb9f87",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Optimizing our parameters\n",
     "We have defined the matrix $\\boldsymbol{X}$ via the equations"
@@ -3307,8 +3205,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29850c49",
-   "metadata": {},
+   "id": "ba858795",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\begin{align*}\n",
@@ -3325,8 +3225,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b1ef4be4",
-   "metadata": {},
+   "id": "be396b49",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "As we noted above, we stayed with a system with the design matrix \n",
     " $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times n}$, that is we have $p=n$. For reasons to come later (algorithmic arguments) we will hereafter define \n",
@@ -3335,8 +3237,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cc56f624",
-   "metadata": {},
+   "id": "b7ea80bb",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Our model for the nuclear binding energies\n",
     "\n",
@@ -3347,9 +3251,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "379ac7d7",
-   "metadata": {},
+   "execution_count": 36,
+   "id": "b1fa766f",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "# Common imports\n",
@@ -3425,16 +3332,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "72c8386b",
-   "metadata": {},
+   "id": "6b78ab4a",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "With $\\boldsymbol{\\beta}\\in {\\mathbb{R}}^{p\\times 1}$, it means that we will hereafter write our equations for the approximation as"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8c1dab4d",
-   "metadata": {},
+   "id": "385cc44e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\boldsymbol{\\tilde{y}}= \\boldsymbol{X}\\boldsymbol{\\beta},\n",
@@ -3443,16 +3354,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e1b09527",
-   "metadata": {},
+   "id": "426e0ba8",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "throughout these lectures."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "df66eff8",
-   "metadata": {},
+   "id": "e588f087",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Optimizing our parameters, more details\n",
     "With the above we use the design matrix to define the approximation $\\boldsymbol{\\tilde{y}}$ via the unknown quantity $\\boldsymbol{\\beta}$ as"
@@ -3460,8 +3375,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e95618ad",
-   "metadata": {},
+   "id": "efa83aff",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\boldsymbol{\\tilde{y}}= \\boldsymbol{X}\\boldsymbol{\\beta},\n",
@@ -3470,16 +3387,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "173c7a34",
-   "metadata": {},
+   "id": "a0e73cd3",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "and in order to find the optimal parameters $\\beta_i$ instead of solving the above linear algebra problem, we define a function which gives a measure of the spread between the values $y_i$ (which represent hopefully the exact values) and the parameterized values $\\tilde{y}_i$, namely"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7b2e428a",
-   "metadata": {},
+   "id": "dad3e4ff",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "C(\\boldsymbol{\\beta})=\\frac{1}{n}\\sum_{i=0}^{n-1}\\left(y_i-\\tilde{y}_i\\right)^2=\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}}\\right)^T\\left(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}}\\right)\\right\\},\n",
@@ -3488,16 +3409,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "515664c6",
-   "metadata": {},
+   "id": "680246cd",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "or using the matrix $\\boldsymbol{X}$ and in a more compact matrix-vector notation as"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "217908f2",
-   "metadata": {},
+   "id": "710acadf",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "C(\\boldsymbol{\\beta})=\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)^T\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)\\right\\}.\n",
@@ -3506,8 +3431,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c12cde40",
-   "metadata": {},
+   "id": "a6a4157e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "This function is one possible way to define the so-called cost function.\n",
     "\n",
@@ -3517,8 +3444,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0fd90d59",
-   "metadata": {},
+   "id": "85f46e12",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "C(\\boldsymbol{\\beta})=\\frac{1}{2n}\\sum_{i=0}^{n-1}\\left(y_i-\\tilde{y}_i\\right)^2,\n",
@@ -3527,16 +3456,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4e91458a",
-   "metadata": {},
+   "id": "b350dcfd",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "since when taking the first derivative with respect to the unknown parameters $\\beta$, the factor of $2$ cancels out."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4334eb96",
-   "metadata": {},
+   "id": "0a930358",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Interpretations and optimizing our parameters\n",
     "\n",
@@ -3545,8 +3478,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "729dadb9",
-   "metadata": {},
+   "id": "eaa5dc70",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "C(\\boldsymbol{\\beta})=\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)^T\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)\\right\\},\n",
@@ -3555,8 +3490,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7cf07294",
-   "metadata": {},
+   "id": "426096c7",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "can be linked to the variance of the quantity $y_i$ if we interpret the latter as the mean value. \n",
     "When linking (see the discussion below) with the maximum likelihood approach below, we will indeed interpret $y_i$ as a mean value"
@@ -3564,8 +3501,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3d6679f5",
-   "metadata": {},
+   "id": "79136235",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "y_{i}=\\langle y_i \\rangle = \\beta_0x_{i,0}+\\beta_1x_{i,1}+\\beta_2x_{i,2}+\\dots+\\beta_{n-1}x_{i,n-1}+\\epsilon_i,\n",
@@ -3574,8 +3513,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7c9773e1",
-   "metadata": {},
+   "id": "4561320b",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "where $\\langle y_i \\rangle$ is the mean value. Keep in mind also that\n",
     "till now we have treated $y_i$ as the exact value. Normally, the\n",
@@ -3591,8 +3532,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27e38e1f",
-   "metadata": {},
+   "id": "d70ec592",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "{\\displaystyle \\min_{\\boldsymbol{\\beta}\\in\n",
@@ -3602,16 +3545,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7bb51ae1",
-   "metadata": {},
+   "id": "9565c742",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "In practical terms it means we will require"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "340cdcc7",
-   "metadata": {},
+   "id": "a5f42b5d",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\frac{\\partial C(\\boldsymbol{\\beta})}{\\partial \\beta_j} = \\frac{\\partial }{\\partial \\beta_j}\\left[ \\frac{1}{n}\\sum_{i=0}^{n-1}\\left(y_i-\\beta_0x_{i,0}-\\beta_1x_{i,1}-\\beta_2x_{i,2}-\\dots-\\beta_{n-1}x_{i,n-1}\\right)^2\\right]=0,\n",
@@ -3620,16 +3567,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1e0fdb28",
-   "metadata": {},
+   "id": "3a5b5c02",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "which results in"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "af91043c",
-   "metadata": {},
+   "id": "24e5b8c3",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\frac{\\partial C(\\boldsymbol{\\beta})}{\\partial \\beta_j} = -\\frac{2}{n}\\left[ \\sum_{i=0}^{n-1}x_{ij}\\left(y_i-\\beta_0x_{i,0}-\\beta_1x_{i,1}-\\beta_2x_{i,2}-\\dots-\\beta_{n-1}x_{i,n-1}\\right)\\right]=0,\n",
@@ -3638,16 +3589,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "621fb35f",
-   "metadata": {},
+   "id": "436e533d",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "or in a matrix-vector form as"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "01147826",
-   "metadata": {},
+   "id": "c259ff65",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\frac{\\partial C(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}} = 0 = \\boldsymbol{X}^T\\left( \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right).\n",
@@ -3656,8 +3611,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e5aeaf7d",
-   "metadata": {},
+   "id": "6448dcf3",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Interpretations and optimizing our parameters\n",
     "We can rewrite"
@@ -3665,8 +3622,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c695cfa9",
-   "metadata": {},
+   "id": "8b2435eb",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\frac{\\partial C(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}} = 0 = \\boldsymbol{X}^T\\left( \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right),\n",
@@ -3675,16 +3634,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "143b1a30",
-   "metadata": {},
+   "id": "f8ee804a",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "as"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c396e9aa",
-   "metadata": {},
+   "id": "65ad1a47",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\boldsymbol{X}^T\\boldsymbol{y} = \\boldsymbol{X}^T\\boldsymbol{X}\\boldsymbol{\\beta},\n",
@@ -3693,16 +3656,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cab9169a",
-   "metadata": {},
+   "id": "daf74af0",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "and if the matrix $\\boldsymbol{X}^T\\boldsymbol{X}$ is invertible we have the solution"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c51e53c6",
-   "metadata": {},
+   "id": "1b4119f3",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\boldsymbol{\\beta} =\\left(\\boldsymbol{X}^T\\boldsymbol{X}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}.\n",
@@ -3711,8 +3678,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c852f0b",
-   "metadata": {},
+   "id": "3f4ad008",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "We note also that since our design matrix is defined as $\\boldsymbol{X}\\in\n",
     "{\\mathbb{R}}^{n\\times p}$, the product $\\boldsymbol{X}^T\\boldsymbol{X} \\in\n",
@@ -3730,8 +3699,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "34e41fcb",
-   "metadata": {},
+   "id": "78a8cce0",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Some useful matrix and vector expressions\n",
     "\n",
@@ -3742,8 +3713,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "74dac7b8",
-   "metadata": {},
+   "id": "46921427",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Interpretations and optimizing our parameters\n",
     "The residuals $\\boldsymbol{\\epsilon}$ are in turn given by"
@@ -3751,8 +3724,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a770a703",
-   "metadata": {},
+   "id": "c2868558",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\boldsymbol{\\epsilon} = \\boldsymbol{y}-\\boldsymbol{\\tilde{y}} = \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta},\n",
@@ -3761,16 +3736,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0c22adf3",
-   "metadata": {},
+   "id": "6ad1c0d6",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "and with"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ebf9b35f",
-   "metadata": {},
+   "id": "9263bd7d",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\boldsymbol{X}^T\\left( \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)= 0,\n",
@@ -3779,16 +3758,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b88e9896",
-   "metadata": {},
+   "id": "bfa3a601",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "we have"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "11c4386a",
-   "metadata": {},
+   "id": "31c49aef",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\boldsymbol{X}^T\\boldsymbol{\\epsilon}=\\boldsymbol{X}^T\\left( \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)= 0,\n",
@@ -3797,8 +3780,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "18fb2110",
-   "metadata": {},
+   "id": "91b31482",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "meaning that the solution for $\\boldsymbol{\\beta}$ is the one which minimizes the residuals.  Later we will link this with the maximum likelihood approach.\n",
     "\n",
@@ -3807,8 +3792,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "11b6aaa2",
-   "metadata": {},
+   "id": "6346b70f",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Own code for Ordinary Least Squares\n",
     "\n",
@@ -3818,9 +3805,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "ba423f48",
-   "metadata": {},
+   "execution_count": 37,
+   "id": "71fe30ea",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "# matrix inversion to find beta\n",
@@ -3831,17 +3821,22 @@
   },
   {
    "cell_type": "markdown",
-   "id": "17e8a4ff",
-   "metadata": {},
+   "id": "7a879ae6",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "Alternatively, you can use the least squares functionality in **Numpy** as"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "c23bf717",
-   "metadata": {},
+   "execution_count": 38,
+   "id": "19826fb5",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "fit = np.linalg.lstsq(X, Energies, rcond =None)[0]\n",
@@ -3850,17 +3845,22 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5d30fc63",
-   "metadata": {},
+   "id": "22b846df",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "And finally we plot our fit with and compare with data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "f5828edf",
-   "metadata": {},
+   "execution_count": 39,
+   "id": "2b502264",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "Masses['Eapprox']  = ytilde\n",
@@ -3879,8 +3879,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5f55d2c5",
-   "metadata": {},
+   "id": "518f98b4",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Adding error analysis and training set up\n",
     "\n",
@@ -3890,9 +3892,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "83def2e2",
-   "metadata": {},
+   "execution_count": 40,
+   "id": "3c151d6e",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "def R2(y_data, y_model):\n",
@@ -3901,17 +3906,22 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9abfbed",
-   "metadata": {},
+   "id": "26425c53",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "and we would be using it as"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "e8a11207",
-   "metadata": {},
+   "execution_count": 41,
+   "id": "a157f7ba",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "print(R2(Energies,ytilde))"
@@ -3919,17 +3929,22 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e3bb01e3",
-   "metadata": {},
+   "id": "35f07dc8",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "We can easily add our **MSE** score as"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "fd4633e8",
-   "metadata": {},
+   "execution_count": 42,
+   "id": "5261540e",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "def MSE(y_data,y_model):\n",
@@ -3941,17 +3956,22 @@
   },
   {
    "cell_type": "markdown",
-   "id": "62e9dfe5",
-   "metadata": {},
+   "id": "ecac1944",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "and finally the relative error as"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "88c19226",
-   "metadata": {},
+   "execution_count": 43,
+   "id": "5bcb3246",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "def RelativeError(y_data,y_model):\n",
@@ -3961,8 +3981,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1b813ef7",
-   "metadata": {},
+   "id": "3bbb96db",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## The $\\chi^2$ function\n",
     "\n",
@@ -3981,8 +4003,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a801e4f3",
-   "metadata": {},
+   "id": "f4859051",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\chi^2(\\boldsymbol{\\beta})=\\frac{1}{n}\\sum_{i=0}^{n-1}\\frac{\\left(y_i-\\tilde{y}_i\\right)^2}{\\sigma_i^2}=\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}}\\right)^T\\frac{1}{\\boldsymbol{\\Sigma^2}}\\left(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}}\\right)\\right\\},\n",
@@ -3991,16 +4015,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8287db3a",
-   "metadata": {},
+   "id": "2dcc7142",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "where the matrix $\\boldsymbol{\\Sigma}$ is a diagonal matrix with $\\sigma_i$ as matrix elements."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d6527b3e",
-   "metadata": {},
+   "id": "a901d25b",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## The $\\chi^2$ function\n",
     "\n",
@@ -4009,8 +4037,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6264d869",
-   "metadata": {},
+   "id": "457f79d5",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\beta_j} = \\frac{\\partial }{\\partial \\beta_j}\\left[ \\frac{1}{n}\\sum_{i=0}^{n-1}\\left(\\frac{y_i-\\beta_0x_{i,0}-\\beta_1x_{i,1}-\\beta_2x_{i,2}-\\dots-\\beta_{n-1}x_{i,n-1}}{\\sigma_i}\\right)^2\\right]=0,\n",
@@ -4019,16 +4049,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "878f2160",
-   "metadata": {},
+   "id": "7482cd0c",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "which results in"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2f1d190f",
-   "metadata": {},
+   "id": "ef2b4a9d",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\beta_j} = -\\frac{2}{n}\\left[ \\sum_{i=0}^{n-1}\\frac{x_{ij}}{\\sigma_i}\\left(\\frac{y_i-\\beta_0x_{i,0}-\\beta_1x_{i,1}-\\beta_2x_{i,2}-\\dots-\\beta_{n-1}x_{i,n-1}}{\\sigma_i}\\right)\\right]=0,\n",
@@ -4037,16 +4071,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e354c8a7",
-   "metadata": {},
+   "id": "8ecbdd95",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "or in a matrix-vector form as"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e6b40c3d",
-   "metadata": {},
+   "id": "b452a84b",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}} = 0 = \\boldsymbol{A}^T\\left( \\boldsymbol{b}-\\boldsymbol{A}\\boldsymbol{\\beta}\\right).\n",
@@ -4055,16 +4093,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "90087fb0",
-   "metadata": {},
+   "id": "48d85467",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "where we have defined the matrix $\\boldsymbol{A} =\\boldsymbol{X}/\\boldsymbol{\\Sigma}$ with matrix elements $a_{ij} = x_{ij}/\\sigma_i$ and the vector $\\boldsymbol{b}$ with elements $b_i = y_i/\\sigma_i$."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9e58ebf8",
-   "metadata": {},
+   "id": "625e33e9",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## The $\\chi^2$ function\n",
     "\n",
@@ -4073,8 +4115,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2cf8400c",
-   "metadata": {},
+   "id": "84fcc90d",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}} = 0 = \\boldsymbol{A}^T\\left( \\boldsymbol{b}-\\boldsymbol{A}\\boldsymbol{\\beta}\\right),\n",
@@ -4083,16 +4127,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a3cdc0df",
-   "metadata": {},
+   "id": "94bb4daf",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "as"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a6edaeda",
-   "metadata": {},
+   "id": "b4c402f1",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\boldsymbol{A}^T\\boldsymbol{b} = \\boldsymbol{A}^T\\boldsymbol{A}\\boldsymbol{\\beta},\n",
@@ -4101,16 +4149,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3580bcbd",
-   "metadata": {},
+   "id": "32aa8c5c",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "and if the matrix $\\boldsymbol{A}^T\\boldsymbol{A}$ is invertible we have the solution"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "38c12137",
-   "metadata": {},
+   "id": "ea9d24d8",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\boldsymbol{\\beta} =\\left(\\boldsymbol{A}^T\\boldsymbol{A}\\right)^{-1}\\boldsymbol{A}^T\\boldsymbol{b}.\n",
@@ -4119,8 +4171,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "80f39365",
-   "metadata": {},
+   "id": "ef204641",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## The $\\chi^2$ function\n",
     "\n",
@@ -4129,8 +4183,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cdc03632",
-   "metadata": {},
+   "id": "840c919d",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\boldsymbol{H} =  \\left(\\boldsymbol{A}^T\\boldsymbol{A}\\right)^{-1},\n",
@@ -4139,16 +4195,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d6f985ae",
-   "metadata": {},
+   "id": "ae496805",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "we have then the following expression for the parameters $\\beta_j$ (the matrix elements of $\\boldsymbol{H}$ are $h_{ij}$)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "55bf4add",
-   "metadata": {},
+   "id": "adeefacb",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\beta_j = \\sum_{k=0}^{p-1}h_{jk}\\sum_{i=0}^{n-1}\\frac{y_i}{\\sigma_i}\\frac{x_{ik}}{\\sigma_i} = \\sum_{k=0}^{p-1}h_{jk}\\sum_{i=0}^{n-1}b_ia_{ik}\n",
@@ -4157,16 +4217,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "73aa8408",
-   "metadata": {},
+   "id": "afbe9b1f",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "We state without proof the expression for the uncertainty  in the parameters $\\beta_j$ as (we leave this as an exercise)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7bbd6f45",
-   "metadata": {},
+   "id": "e40a4734",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\sigma^2(\\beta_j) = \\sum_{i=0}^{n-1}\\sigma_i^2\\left( \\frac{\\partial \\beta_j}{\\partial y_i}\\right)^2,\n",
@@ -4175,16 +4239,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fa31107f",
-   "metadata": {},
+   "id": "359b8cd2",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "resulting in"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5c862efa",
-   "metadata": {},
+   "id": "3d2948ce",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\sigma^2(\\beta_j) = \\left(\\sum_{k=0}^{p-1}h_{jk}\\sum_{i=0}^{n-1}a_{ik}\\right)\\left(\\sum_{l=0}^{p-1}h_{jl}\\sum_{m=0}^{n-1}a_{ml}\\right) = h_{jj}!\n",
@@ -4193,8 +4261,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ebead3e5",
-   "metadata": {},
+   "id": "7784d352",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## The $\\chi^2$ function\n",
     "The first step here is to approximate the function $y$ with a first-order polynomial, that is we write"
@@ -4202,8 +4272,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2c8e974e",
-   "metadata": {},
+   "id": "c31793f9",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "y=y(x) \\rightarrow y(x_i) \\approx \\beta_0+\\beta_1 x_i.\n",
@@ -4212,16 +4284,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a13a7fff",
-   "metadata": {},
+   "id": "06e4b1dd",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "By computing the derivatives of $\\chi^2$ with respect to $\\beta_0$ and $\\beta_1$ show that these are given by"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fdf4fbab",
-   "metadata": {},
+   "id": "e357a5c7",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\beta_0} = -2\\left[ \\frac{1}{n}\\sum_{i=0}^{n-1}\\left(\\frac{y_i-\\beta_0-\\beta_1x_{i}}{\\sigma_i^2}\\right)\\right]=0,\n",
@@ -4230,16 +4306,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "38c90faf",
-   "metadata": {},
+   "id": "61b42515",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "and"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "71848e51",
-   "metadata": {},
+   "id": "a89dd493",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\beta_1} = -\\frac{2}{n}\\left[ \\sum_{i=0}^{n-1}x_i\\left(\\frac{y_i-\\beta_0-\\beta_1x_{i}}{\\sigma_i^2}\\right)\\right]=0.\n",
@@ -4248,8 +4328,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5e87c520",
-   "metadata": {},
+   "id": "7b669ac8",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## The $\\chi^2$ function\n",
     "\n",
@@ -4259,8 +4341,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d5107c6f",
-   "metadata": {},
+   "id": "52f26226",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\gamma =  \\sum_{i=0}^{n-1}\\frac{1}{\\sigma_i^2},\n",
@@ -4269,8 +4353,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9b00454a",
-   "metadata": {},
+   "id": "4abc0867",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\gamma_x =  \\sum_{i=0}^{n-1}\\frac{x_{i}}{\\sigma_i^2},\n",
@@ -4279,8 +4365,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "af23f553",
-   "metadata": {},
+   "id": "ec3bb850",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\gamma_y = \\sum_{i=0}^{n-1}\\left(\\frac{y_i}{\\sigma_i^2}\\right),\n",
@@ -4289,8 +4377,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "50af34fe",
-   "metadata": {},
+   "id": "43db8d01",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\gamma_{xx} =  \\sum_{i=0}^{n-1}\\frac{x_ix_{i}}{\\sigma_i^2},\n",
@@ -4299,8 +4389,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e19ee9dd",
-   "metadata": {},
+   "id": "8ca6ded9",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\gamma_{xy} = \\sum_{i=0}^{n-1}\\frac{y_ix_{i}}{\\sigma_i^2},\n",
@@ -4309,16 +4401,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1db70f83",
-   "metadata": {},
+   "id": "6eb5060b",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "we obtain"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "309219f7",
-   "metadata": {},
+   "id": "4a9bffc7",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\beta_0 = \\frac{\\gamma_{xx}\\gamma_y-\\gamma_x\\gamma_y}{\\gamma\\gamma_{xx}-\\gamma_x^2},\n",
@@ -4327,8 +4423,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e424705",
-   "metadata": {},
+   "id": "aed06458",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\beta_1 = \\frac{\\gamma_{xy}\\gamma-\\gamma_x\\gamma_y}{\\gamma\\gamma_{xx}-\\gamma_x^2}.\n",
@@ -4337,8 +4435,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "94c1e7d5",
-   "metadata": {},
+   "id": "6ab6bc12",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "This approach (different linear and non-linear regression) suffers\n",
     "often from both being underdetermined and overdetermined in the\n",
@@ -4348,8 +4448,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4c6dc9f2",
-   "metadata": {},
+   "id": "c6990945",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Fitting an Equation of State for Dense Nuclear Matter\n",
     "\n",
@@ -4373,17 +4475,22 @@
   },
   {
    "cell_type": "markdown",
-   "id": "622e36e2",
-   "metadata": {},
+   "id": "045ef3aa",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## The code"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "5fec1708",
-   "metadata": {},
+   "execution_count": 44,
+   "id": "64257f25",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "# Common imports\n",
@@ -4475,8 +4582,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e75e727f",
-   "metadata": {},
+   "id": "07198eed",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "The above simple polynomial in density $\\rho$ gives an excellent fit\n",
     "to the data. \n",
@@ -4488,8 +4597,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cf01e24b",
-   "metadata": {},
+   "id": "f9fd388c",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Splitting our Data in Training and Test data\n",
     "\n",
@@ -4507,9 +4618,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "32b65d12",
-   "metadata": {},
+   "execution_count": 45,
+   "id": "c447efb1",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "import os\n",
@@ -4580,8 +4694,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2fdbbbbc",
-   "metadata": {},
+   "id": "f4a8c4f8",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Exercises\n",
     "\n",
@@ -4590,8 +4706,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55d2839c",
-   "metadata": {},
+   "id": "fca6ce1d",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Exercise 1: Setting up various Python environments\n",
     "\n",
@@ -4657,8 +4775,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "92c1bca0",
-   "metadata": {},
+   "id": "8375e0ee",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Exercise 2: making your own data and exploring scikit-learn\n",
     "\n",
@@ -4668,9 +4788,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "74dd9421",
-   "metadata": {},
+   "execution_count": 46,
+   "id": "995cb4f3",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "x = np.random.rand(100,1)\n",
@@ -4679,8 +4802,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2d7955a0",
-   "metadata": {},
+   "id": "f6b03600",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "1. Write your own code (following the examples under the [regression notes](https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter1.html)) for computing the parametrization of the data set fitting a second-order polynomial. \n",
     "\n",
@@ -4691,8 +4816,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c29304b4",
-   "metadata": {},
+   "id": "16d8ae1e",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "MSE(\\boldsymbol{y},\\boldsymbol{\\tilde{y}}) = \\frac{1}{n}\n",
@@ -4702,8 +4829,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "06b0b5bf",
-   "metadata": {},
+   "id": "bb8f26d6",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "and the $R^2$ score function.\n",
     "If $\\tilde{\\boldsymbol{y}}_i$ is the predicted value of the $i-th$ sample and $y_i$ is the corresponding true value, then the score $R^2$ is defined as"
@@ -4711,8 +4840,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d5819abe",
-   "metadata": {},
+   "id": "21ae122a",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "R^2(\\boldsymbol{y}, \\tilde{\\boldsymbol{y}}) = 1 - \\frac{\\sum_{i=0}^{n - 1} (y_i - \\tilde{y}_i)^2}{\\sum_{i=0}^{n - 1} (y_i - \\bar{y})^2},\n",
@@ -4721,16 +4852,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "95b1af41",
-   "metadata": {},
+   "id": "70681940",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "where we have defined the mean value  of $\\boldsymbol{y}$ as"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "caba64c0",
-   "metadata": {},
+   "id": "3ad382fa",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "$$\n",
     "\\bar{y} =  \\frac{1}{n} \\sum_{i=0}^{n - 1} y_i.\n",
@@ -4739,8 +4874,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d20f29ea",
-   "metadata": {},
+   "id": "d1315676",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "You can use the functionality included in scikit-learn. If you feel for it, you can use your own program and define functions which compute the above two functions. \n",
     "Discuss the meaning of these results. Try also to vary the coefficient in front of the added stochastic noise term and discuss the quality of the fits."
@@ -4748,8 +4885,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "69be1267",
-   "metadata": {},
+   "id": "3629c154",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "## Exercise 3: Split data in test and training data\n",
     "\n",
@@ -4764,9 +4903,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "52031830",
-   "metadata": {},
+   "execution_count": 47,
+   "id": "cecae264",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
    "outputs": [],
    "source": [
     "np.random.seed()\n",
@@ -4778,16 +4920,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9ac507e5",
-   "metadata": {},
+   "id": "f040a1cd",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "where $y$ is the function we want to fit with a given polynomial."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "31a568f4",
-   "metadata": {},
+   "id": "7730d442",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "**a)**\n",
     "Write a first code which sets up a design matrix $X$ defined by a fifth-order polynomial and split your data set in training and test data."
@@ -4795,8 +4941,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1086d6ac",
-   "metadata": {},
+   "id": "ab675e19",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "**b)**\n",
     "Write thereafter (using either **scikit-learn** or your matrix inversion code using for example **numpy**)\n",
@@ -4805,33 +4953,17 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7b15bc16",
-   "metadata": {},
+   "id": "da90b2d9",
+   "metadata": {
+    "editable": true
+   },
    "source": [
     "**c)**\n",
     "Add now a model which allows you to make polynomials up to degree $15$.  Perform a standard OLS fitting of the training data and compute the MSE for the training and test data and plot both test and training data MSE as functions of the polynomial degree. Compare what you see with Figure 2.11 of Hastie et al. Comment your results. For which polynomial degree do you find an optimal MSE (smallest value)?"
    ]
   }
  ],
- "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": "3.9.18"
-  }
- },
+ "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 5
 }
diff --git a/doc/src/week34/week34.do.txt b/doc/src/week34/week34.do.txt
index 7bb22b719..befb7e0a8 100644
--- a/doc/src/week34/week34.do.txt
+++ b/doc/src/week34/week34.do.txt
@@ -1,6 +1,6 @@
 TITLE: Week 34: Introduction to the course, Logistics and Practicalities
 AUTHOR: Morten Hjorth-Jensen  {copyright, 1999-present|CC BY-NC} at Department of Physics and Center for Computing in Science Education, University of Oslo, Norway & Department of Physics and Astronomy and Facility for Rare Isotope Beams, Michigan State University, USA
-DATE: Week 34, August 21-25, 2023
+DATE: Week 34, August 19-23, 2024
 
 
 
@@ -13,8 +13,8 @@ o There are four groups:
   * Tuesdays 815am-12pm and 1215pm-4pm
   * Wednesdays 815am-12pm and 1215pm-4pm.
 
-o On Thursdays we have a regular lecture. These lectures start at 1215pm and end at 2pm and serve the aims of giving an overview over various topics. These lectures will also be recorded.
-  * "Link to recording of lecture":"https://youtu.be/OAslWa-NIkQ"
+o On Mondays  we have a regular lecture which will be organized as a mix of active learning sessions and lecturing. These lectures/active learning sessions start at 1015am and end at 12pm and serve the aims of giving an overview over various topics. These lectures will also be recorded.
+  * "Link to recording of lecture TBA":"https://youtu.be/"
 The labs are also available till 6pm Tuesdays and Wednesdays. Videos and learning material with reading suggestions will be made available before each week starts.
 
 
@@ -25,9 +25,10 @@ The labs are also available till 6pm Tuesdays and Wednesdays. Videos and learnin
 ===== Schedule first week =====
 
 !bblock
-  * August 22: Presentation of the course, aims and content. Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.
-  * August 23: Presentation of the course, aims and content. Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.
-  * August 24: Lecture: Linear regression, examples and theory 
+  * August 19: Lecture: Presentation of course, Linear regression, examples and theory 
+  * August 20: Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.
+  * August 23: Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.
+
 !eblock
 
 
@@ -37,8 +38,8 @@ The labs are also available till 6pm Tuesdays and Wednesdays. Videos and learnin
 ===== Lectures and ComputerLab =====
 
 !bblock
+  * Mondays: regular lectures/active lerning sessions (10.15am-12pm) 
   * The sessions on Tuesdays and Wednesdays last four hours and will include partly lectures and discussions in the beginning.
-  * Thursdays: regular lectures (12.15pm-2pm) 
   * Weekly reading assignments and videos needed to solve projects and exercises.
   * Weekly exercises. You can hand in exercises if you want and get an extra score, see below.
   * Detailed lecture notes, exercises, all programs presented, projects etc can be found at the homepage of the course.
@@ -81,8 +82,8 @@ The labs are also available till 6pm Tuesdays and Wednesdays. Videos and learnin
 * Fahimeh Najafi, fahimeh.najafi@fys.uio.no
 * Mia-Katrin Ose Kvalsund, m.k.o.kvalsund@fys.uio.no
 * Karl Henrik Fredly, k.h.fredly@fys.uio.no
-* Adam Jakobsen, adam.jakobsen@fys.uio.no
-* Daniel Haas Beccatini Lima, d.h.b.lima@fys.uio.no
+* TBA
+* TBA
 
 
 !split