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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "29616fbf",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)\n",
+    "doconce format html exercisesweek47.do.txt  -->\n",
+    "<!-- dom:TITLE: Exercise week 47 -->"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4f3fe3f",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "# Exercise week 47\n",
+    "**November 20-24, 2023**\n",
+    "\n",
+    "Date: **Deadline is Sunday November 26 at midnight**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1f597b7",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "# Overarching aims of the exercises this week\n",
+    "\n",
+    "The exercise this week is a simple course survey and feedback.  This\n",
+    "is important for us in order to improve our teaching material, the\n",
+    "active learning format and anything else related to a succesful\n",
+    "mastering of central machine learning methods and their applications."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7497591b",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Why did you choose this course?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c08143f",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### What was your programming knowledge before you started?\n",
+    "\n",
+    "And do you feel this course added to your programming competences and skills?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9783d8a7",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### How do you judge your own level of knowledge on machine learning before and after this course?\n",
+    "\n",
+    "Here you can discuss your level of skill/knowledge at start of course\n",
+    "and at the end of the course and how these matched the level of\n",
+    "skill/knowledge needed to complete the projects."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b0d5c47",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Did the projects and the teaching material allow you to deepen your insights about Machine Learning?\n",
+    "\n",
+    "Feel free to comment here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ead8d47",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Project based teaching and active learning\n",
+    "\n",
+    "This is a project based course and we as teachers would like to keep\n",
+    "it as it is since we see very clearly that people who attend this\n",
+    "course have a very good learning outcome. Project based courses are\n",
+    "however demanding (and expensive seen from the university admin) when\n",
+    "it comes to proper feedback and evaluations. Feel free to discuss\n",
+    "whether you found a project-based and active learning approach\n",
+    "useful. Feel also free to comment upon things we can improve upon or\n",
+    "alternative ways to assess whether the learning outcomes have been\n",
+    "achieved. Would you for example prefer a standard 4 hours written exam\n",
+    "be something you would prefer?  Or other alternatives to projects? We\n",
+    "would very much value your thoughts here since projects are an\n",
+    "essential part of this course."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "746bd238",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Usefulness of the weekly exercises\n",
+    "\n",
+    "Did the weekly exercises help in getting started with the projects?\n",
+    "How relevant where they for solving the projects?  Feel free to\n",
+    "elaborate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ce96822",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Active learning/lab sessions and lectures\n",
+    "\n",
+    "Was there a good link between lectures and active learning sessions?\n",
+    "Would you prefer an active learning environment only with no lectures\n",
+    "or would you prefer a more lecture based format with lab sessions only\n",
+    "(that is no discussion at the beginning of the lab sessions)? Feel\n",
+    "free to comment."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5dc0637a",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### How would you improve this course?\n",
+    "\n",
+    "Are there topics which are missing, topics which could have been\n",
+    "omitted and/or discussed in more depth? Feel free to add your comments\n",
+    "here such as how to improve to teaching material and more."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a98adf6c",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Then some basic questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a935279",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Which is your preferred information chanel, Canvas, Discord, mail or other?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41592552",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Was the weekly update with plans etc useful?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e79a2360",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Was it easy to access the course material?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8bc4dfc9",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Which resources and tools did you use? Jupyter-notebooks, GitHub, the various textbooks we have recommended, etc etc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf415075",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### If you did not attend the lectures or the active learning/lab sessions, which resources did you use?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db3db20a",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Any other topics, impressions, ideas etc you would like to share with us?"
+   ]
+  }
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/LectureNotes/_build/html/exercisesweek47.html b/doc/LectureNotes/_build/html/exercisesweek47.html
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+
+<!DOCTYPE html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
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+    <link rel="next" title="Project 1 on Machine Learning, deadline October 9 (midnight), 2023" href="project1.html" />
+    <link rel="prev" title="Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course" href="week47.html" />
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+      
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+      <h1 class="site-logo" id="site-title">Applied Data Analysis and Machine Learning</h1>
+      
+    </a>
+</div><form class="bd-search d-flex align-items-center" action="search.html" method="get">
+  <i class="icon fas fa-search"></i>
+  <input type="search" class="form-control" name="q" id="search-input" placeholder="Search this book..." aria-label="Search this book..." autocomplete="off" >
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+    <div class="bd-toc-item active">
+        <ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="intro.html">
+   Applied Data Analysis and Machine Learning
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  About the course
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="schedule.html">
+   Teaching schedule with links to material
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="teachers.html">
+   Teachers and Grading
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="textbooks.html">
+   Textbooks
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Review of Statistics with Resampling Techniques and Linear Algebra
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="statistics.html">
+   1. Elements of Probability Theory and Statistical Data Analysis
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="linalg.html">
+   2. Linear Algebra, Handling of Arrays and more Python Features
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  From Regression to Support Vector Machines
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter1.html">
+   3. Linear Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter2.html">
+   4. Ridge and Lasso Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter3.html">
+   5. Resampling Methods
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter4.html">
+   6. Logistic Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapteroptimization.html">
+   7. Optimization, the central part of any Machine Learning algortithm
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter5.html">
+   8. Support Vector Machines, overarching aims
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Decision Trees, Ensemble Methods and Boosting
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter6.html">
+   9. Decision trees, overarching aims
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter7.html">
+   10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Dimensionality Reduction
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter8.html">
+   11. Basic ideas of the Principal Component Analysis (PCA)
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="clustering.html">
+   12. Clustering and Unsupervised Learning
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Deep Learning Methods
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter9.html">
+   13. Neural networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter10.html">
+   14. Building a Feed Forward Neural Network
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter11.html">
+   15. Solving Differential Equations  with Deep Learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter12.html">
+   16. Convolutional Neural Networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter13.html">
+   17. Recurrent neural networks: Overarching view
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Weekly material, notes and exercises
+ </span>
+</p>
+<ul class="current nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek34.html">
+   Exercises week 34
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week34.html">
+   Week 34: Introduction to the course, Logistics and Practicalities
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek35.html">
+   Exercises week 35
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week35.html">
+   Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek36.html">
+   Exercises week 36
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week36.html">
+   Week 36: Statistical interpretation of Linear Regression and Resampling techniques
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek37.html">
+   Exercises week 37
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week37.html">
+   Week 37: Statistical interpretations and Resampling Methods
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek38.html">
+   Exercises week 38
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week38.html">
+   Week 38: Logistic Regression and Optimization
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek39.html">
+   Exercises week 39
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week39.html">
+   Week 39: Optimization and  Gradient Methods
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week40.html">
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek41.html">
+   Exercises week 41
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week41.html">
+   Week 41 Neural networks and constructing a neural network code
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek42.html">
+   Exercises week 42
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week42.html">
+   Week 42 Constructing a Neural Network code with introduction to Tensor flow
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek43.html">
+   Exercises weeks 43 and 44
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week43.html">
+   Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week44.html">
+   Week 44,  Convolutional Neural Networks (CNN)
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week45.html">
+   Week 45,  Recurrent Neural Networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week46.html">
+   Week 46: Decision Trees, Ensemble methods  and Random Forests
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week47.html">
+   Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods   and Summary of Course
+  </a>
+ </li>
+ <li class="toctree-l1 current active">
+  <a class="current reference internal" href="#">
+   Exercise week 47
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Projects
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="project1.html">
+   Project 1 on Machine Learning, deadline October 9 (midnight), 2023
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="project2.html">
+   Project 2 on Machine Learning, deadline November 17 (Midnight)
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="project3.html">
+   Project 3 on Machine Learning, deadline December 18 (midnight), 2023
+  </a>
+ </li>
+</ul>
+
+    </div>
+</nav> <!-- To handle the deprecated key -->
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+        <!-- Download PDF via print -->
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+                onclick="printPdf(this)" data-toggle="tooltip" data-placement="left">.pdf</button>
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+</div>
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+
+<a class="full-screen-button"><button type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip"
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+        title="Fullscreen mode"><i
+            class="fas fa-expand"></i></button></a>
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+        <!-- Table of contents -->
+        <div class="d-none d-md-block col-md-2 bd-toc show noprint">
+            
+            <div class="tocsection onthispage pt-5 pb-3">
+                <i class="fas fa-list"></i> Contents
+            </div>
+            <nav id="bd-toc-nav" aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+ <li class="toc-h1 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#">
+   Exercise week 47
+  </a>
+ </li>
+ <li class="toc-h1 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#overarching-aims-of-the-exercises-this-week">
+   Overarching aims of the exercises this week
+  </a>
+  <ul class="visible nav section-nav flex-column">
+   <li class="toc-h2 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#why-did-you-choose-this-course">
+     Why did you choose this course?
+    </a>
+   </li>
+   <li class="toc-h2 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#what-was-your-programming-knowledge-before-you-started">
+     What was your programming knowledge before you started?
+    </a>
+   </li>
+   <li class="toc-h2 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#how-do-you-judge-your-own-level-of-knowledge-on-machine-learning-before-and-after-this-course">
+     How do you judge your own level of knowledge on machine learning before and after this course?
+    </a>
+   </li>
+   <li class="toc-h2 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#did-the-projects-and-the-teaching-material-allow-you-to-deepen-your-insights-about-machine-learning">
+     Did the projects and the teaching material allow you to deepen your insights about Machine Learning?
+    </a>
+   </li>
+   <li class="toc-h2 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#project-based-teaching-and-active-learning">
+     Project based teaching and active learning
+    </a>
+   </li>
+   <li class="toc-h2 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#usefulness-of-the-weekly-exercises">
+     Usefulness of the weekly exercises
+    </a>
+   </li>
+   <li class="toc-h2 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#active-learning-lab-sessions-and-lectures">
+     Active learning/lab sessions and lectures
+    </a>
+   </li>
+   <li class="toc-h2 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#how-would-you-improve-this-course">
+     How would you improve this course?
+    </a>
+   </li>
+   <li class="toc-h2 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#then-some-basic-questions">
+     Then some basic questions
+    </a>
+    <ul class="nav section-nav flex-column">
+     <li class="toc-h3 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#which-is-your-preferred-information-chanel-canvas-discord-mail-or-other">
+       Which is your preferred information chanel, Canvas, Discord, mail or other?
+      </a>
+     </li>
+     <li class="toc-h3 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#was-the-weekly-update-with-plans-etc-useful">
+       Was the weekly update with plans etc useful?
+      </a>
+     </li>
+     <li class="toc-h3 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#was-it-easy-to-access-the-course-material">
+       Was it easy to access the course material?
+      </a>
+     </li>
+     <li class="toc-h3 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#which-resources-and-tools-did-you-use-jupyter-notebooks-github-the-various-textbooks-we-have-recommended-etc-etc">
+       Which resources and tools did you use? Jupyter-notebooks, GitHub, the various textbooks we have recommended, etc etc
+      </a>
+     </li>
+     <li class="toc-h3 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#if-you-did-not-attend-the-lectures-or-the-active-learning-lab-sessions-which-resources-did-you-use">
+       If you did not attend the lectures or the active learning/lab sessions, which resources did you use?
+      </a>
+     </li>
+     <li class="toc-h3 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#any-other-topics-impressions-ideas-etc-you-would-like-to-share-with-us">
+       Any other topics, impressions, ideas etc you would like to share with us?
+      </a>
+     </li>
+    </ul>
+   </li>
+  </ul>
+ </li>
+</ul>
+
+            </nav>
+        </div>
+    </div>
+</div>
+    <div id="main-content" class="row">
+        <div class="col-12 col-md-9 pl-md-3 pr-md-0">
+            <!-- Table of contents that is only displayed when printing the page -->
+            <div id="jb-print-docs-body" class="onlyprint">
+                <h1>Exercise week 47</h1>
+                <!-- Table of contents -->
+                <div id="print-main-content">
+                    <div id="jb-print-toc">
+                        
+                        <div>
+                            <h2> Contents </h2>
+                        </div>
+                        <nav aria-label="Page">
+                            <ul class="visible nav section-nav flex-column">
+ <li class="toc-h1 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#">
+   Exercise week 47
+  </a>
+ </li>
+ <li class="toc-h1 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#overarching-aims-of-the-exercises-this-week">
+   Overarching aims of the exercises this week
+  </a>
+  <ul class="visible nav section-nav flex-column">
+   <li class="toc-h2 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#why-did-you-choose-this-course">
+     Why did you choose this course?
+    </a>
+   </li>
+   <li class="toc-h2 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#what-was-your-programming-knowledge-before-you-started">
+     What was your programming knowledge before you started?
+    </a>
+   </li>
+   <li class="toc-h2 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#how-do-you-judge-your-own-level-of-knowledge-on-machine-learning-before-and-after-this-course">
+     How do you judge your own level of knowledge on machine learning before and after this course?
+    </a>
+   </li>
+   <li class="toc-h2 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#did-the-projects-and-the-teaching-material-allow-you-to-deepen-your-insights-about-machine-learning">
+     Did the projects and the teaching material allow you to deepen your insights about Machine Learning?
+    </a>
+   </li>
+   <li class="toc-h2 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#project-based-teaching-and-active-learning">
+     Project based teaching and active learning
+    </a>
+   </li>
+   <li class="toc-h2 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#usefulness-of-the-weekly-exercises">
+     Usefulness of the weekly exercises
+    </a>
+   </li>
+   <li class="toc-h2 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#active-learning-lab-sessions-and-lectures">
+     Active learning/lab sessions and lectures
+    </a>
+   </li>
+   <li class="toc-h2 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#how-would-you-improve-this-course">
+     How would you improve this course?
+    </a>
+   </li>
+   <li class="toc-h2 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#then-some-basic-questions">
+     Then some basic questions
+    </a>
+    <ul class="nav section-nav flex-column">
+     <li class="toc-h3 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#which-is-your-preferred-information-chanel-canvas-discord-mail-or-other">
+       Which is your preferred information chanel, Canvas, Discord, mail or other?
+      </a>
+     </li>
+     <li class="toc-h3 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#was-the-weekly-update-with-plans-etc-useful">
+       Was the weekly update with plans etc useful?
+      </a>
+     </li>
+     <li class="toc-h3 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#was-it-easy-to-access-the-course-material">
+       Was it easy to access the course material?
+      </a>
+     </li>
+     <li class="toc-h3 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#which-resources-and-tools-did-you-use-jupyter-notebooks-github-the-various-textbooks-we-have-recommended-etc-etc">
+       Which resources and tools did you use? Jupyter-notebooks, GitHub, the various textbooks we have recommended, etc etc
+      </a>
+     </li>
+     <li class="toc-h3 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#if-you-did-not-attend-the-lectures-or-the-active-learning-lab-sessions-which-resources-did-you-use">
+       If you did not attend the lectures or the active learning/lab sessions, which resources did you use?
+      </a>
+     </li>
+     <li class="toc-h3 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#any-other-topics-impressions-ideas-etc-you-would-like-to-share-with-us">
+       Any other topics, impressions, ideas etc you would like to share with us?
+      </a>
+     </li>
+    </ul>
+   </li>
+  </ul>
+ </li>
+</ul>
+
+                        </nav>
+                    </div>
+                </div>
+            </div>
+            
+              <div>
+                
+  <!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)
+doconce format html exercisesweek47.do.txt  -->
+<!-- dom:TITLE: Exercise week 47 --><div class="tex2jax_ignore mathjax_ignore section" id="exercise-week-47">
+<h1>Exercise week 47<a class="headerlink" href="#exercise-week-47" title="Permalink to this headline">¶</a></h1>
+<p><strong>November 20-24, 2023</strong></p>
+<p>Date: <strong>Deadline is Sunday November 26 at midnight</strong></p>
+</div>
+<div class="tex2jax_ignore mathjax_ignore section" id="overarching-aims-of-the-exercises-this-week">
+<h1>Overarching aims of the exercises this week<a class="headerlink" href="#overarching-aims-of-the-exercises-this-week" title="Permalink to this headline">¶</a></h1>
+<p>The exercise this week is a simple course survey and feedback.  This
+is important for us in order to improve our teaching material, the
+active learning format and anything else related to a succesful
+mastering of central machine learning methods and their applications.</p>
+<div class="section" id="why-did-you-choose-this-course">
+<h2>Why did you choose this course?<a class="headerlink" href="#why-did-you-choose-this-course" title="Permalink to this headline">¶</a></h2>
+</div>
+<div class="section" id="what-was-your-programming-knowledge-before-you-started">
+<h2>What was your programming knowledge before you started?<a class="headerlink" href="#what-was-your-programming-knowledge-before-you-started" title="Permalink to this headline">¶</a></h2>
+<p>And do you feel this course added to your programming competences and skills?</p>
+</div>
+<div class="section" id="how-do-you-judge-your-own-level-of-knowledge-on-machine-learning-before-and-after-this-course">
+<h2>How do you judge your own level of knowledge on machine learning before and after this course?<a class="headerlink" href="#how-do-you-judge-your-own-level-of-knowledge-on-machine-learning-before-and-after-this-course" title="Permalink to this headline">¶</a></h2>
+<p>Here you can discuss your level of skill/knowledge at start of course
+and at the end of the course and how these matched the level of
+skill/knowledge needed to complete the projects.</p>
+</div>
+<div class="section" id="did-the-projects-and-the-teaching-material-allow-you-to-deepen-your-insights-about-machine-learning">
+<h2>Did the projects and the teaching material allow you to deepen your insights about Machine Learning?<a class="headerlink" href="#did-the-projects-and-the-teaching-material-allow-you-to-deepen-your-insights-about-machine-learning" title="Permalink to this headline">¶</a></h2>
+<p>Feel free to comment here.</p>
+</div>
+<div class="section" id="project-based-teaching-and-active-learning">
+<h2>Project based teaching and active learning<a class="headerlink" href="#project-based-teaching-and-active-learning" title="Permalink to this headline">¶</a></h2>
+<p>This is a project based course and we as teachers would like to keep
+it as it is since we see very clearly that people who attend this
+course have a very good learning outcome. Project based courses are
+however demanding (and expensive seen from the university admin) when
+it comes to proper feedback and evaluations. Feel free to discuss
+whether you found a project-based and active learning approach
+useful. Feel also free to comment upon things we can improve upon or
+alternative ways to assess whether the learning outcomes have been
+achieved. Would you for example prefer a standard 4 hours written exam
+be something you would prefer?  Or other alternatives to projects? We
+would very much value your thoughts here since projects are an
+essential part of this course.</p>
+</div>
+<div class="section" id="usefulness-of-the-weekly-exercises">
+<h2>Usefulness of the weekly exercises<a class="headerlink" href="#usefulness-of-the-weekly-exercises" title="Permalink to this headline">¶</a></h2>
+<p>Did the weekly exercises help in getting started with the projects?
+How relevant where they for solving the projects?  Feel free to
+elaborate</p>
+</div>
+<div class="section" id="active-learning-lab-sessions-and-lectures">
+<h2>Active learning/lab sessions and lectures<a class="headerlink" href="#active-learning-lab-sessions-and-lectures" title="Permalink to this headline">¶</a></h2>
+<p>Was there a good link between lectures and active learning sessions?
+Would you prefer an active learning environment only with no lectures
+or would you prefer a more lecture based format with lab sessions only
+(that is no discussion at the beginning of the lab sessions)? Feel
+free to comment.</p>
+</div>
+<div class="section" id="how-would-you-improve-this-course">
+<h2>How would you improve this course?<a class="headerlink" href="#how-would-you-improve-this-course" title="Permalink to this headline">¶</a></h2>
+<p>Are there topics which are missing, topics which could have been
+omitted and/or discussed in more depth? Feel free to add your comments
+here such as how to improve to teaching material and more.</p>
+</div>
+<div class="section" id="then-some-basic-questions">
+<h2>Then some basic questions<a class="headerlink" href="#then-some-basic-questions" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="which-is-your-preferred-information-chanel-canvas-discord-mail-or-other">
+<h3>Which is your preferred information chanel, Canvas, Discord, mail or other?<a class="headerlink" href="#which-is-your-preferred-information-chanel-canvas-discord-mail-or-other" title="Permalink to this headline">¶</a></h3>
+</div>
+<div class="section" id="was-the-weekly-update-with-plans-etc-useful">
+<h3>Was the weekly update with plans etc useful?<a class="headerlink" href="#was-the-weekly-update-with-plans-etc-useful" title="Permalink to this headline">¶</a></h3>
+</div>
+<div class="section" id="was-it-easy-to-access-the-course-material">
+<h3>Was it easy to access the course material?<a class="headerlink" href="#was-it-easy-to-access-the-course-material" title="Permalink to this headline">¶</a></h3>
+</div>
+<div class="section" id="which-resources-and-tools-did-you-use-jupyter-notebooks-github-the-various-textbooks-we-have-recommended-etc-etc">
+<h3>Which resources and tools did you use? Jupyter-notebooks, GitHub, the various textbooks we have recommended, etc etc<a class="headerlink" href="#which-resources-and-tools-did-you-use-jupyter-notebooks-github-the-various-textbooks-we-have-recommended-etc-etc" title="Permalink to this headline">¶</a></h3>
+</div>
+<div class="section" id="if-you-did-not-attend-the-lectures-or-the-active-learning-lab-sessions-which-resources-did-you-use">
+<h3>If you did not attend the lectures or the active learning/lab sessions, which resources did you use?<a class="headerlink" href="#if-you-did-not-attend-the-lectures-or-the-active-learning-lab-sessions-which-resources-did-you-use" title="Permalink to this headline">¶</a></h3>
+</div>
+<div class="section" id="any-other-topics-impressions-ideas-etc-you-would-like-to-share-with-us">
+<h3>Any other topics, impressions, ideas etc you would like to share with us?<a class="headerlink" href="#any-other-topics-impressions-ideas-etc-you-would-like-to-share-with-us" title="Permalink to this headline">¶</a></h3>
+</div>
+</div>
+</div>
+
+    <script type="text/x-thebe-config">
+    {
+        requestKernel: true,
+        binderOptions: {
+            repo: "binder-examples/jupyter-stacks-datascience",
+            ref: "master",
+        },
+        codeMirrorConfig: {
+            theme: "abcdef",
+            mode: "python"
+        },
+        kernelOptions: {
+            kernelName: "python3",
+            path: "./."
+        },
+        predefinedOutput: true
+    }
+    </script>
+    <script>kernelName = 'python3'</script>
+
+              </div>
+              
+            
+                <!-- Previous / next buttons -->
+<div class='prev-next-area'> 
+    <a class='left-prev' id="prev-link" href="week47.html" title="previous page">
+        <i class="fas fa-angle-left"></i>
+        <div class="prev-next-info">
+            <p class="prev-next-subtitle">previous</p>
+            <p class="prev-next-title">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods   and Summary of Course</p>
+        </div>
+    </a>
+    <a class='right-next' id="next-link" href="project1.html" title="next page">
+    <div class="prev-next-info">
+        <p class="prev-next-subtitle">next</p>
+        <p class="prev-next-title">Project 1 on Machine Learning, deadline October 9 (midnight), 2023</p>
+    </div>
+    <i class="fas fa-angle-right"></i>
+    </a>
+</div>
+            
+        </div>
+    </div>
+    <footer class="footer">
+  <p>
+    
+      By Morten Hjorth-Jensen<br/>
+    
+        &copy; Copyright 2021.<br/>
+  </p>
+</footer>
+</main>
+
+
+      </div>
+    </div>
+  
+  <script src="_static/js/index.be7d3bbb2ef33a8344ce.js"></script>
+
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+</html>
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diff --git a/doc/LectureNotes/_build/html/genindex.html b/doc/LectureNotes/_build/html/genindex.html
index 9ccf71fa9..0996b4bf9 100644
--- a/doc/LectureNotes/_build/html/genindex.html
+++ b/doc/LectureNotes/_build/html/genindex.html
@@ -359,6 +359,11 @@ const thebe_selector_output = ".output, .cell_output"
    Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods   and Summary of Course
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek47.html">
+   Exercise week 47
+  </a>
+ </li>
 </ul>
 <p aria-level="2" class="caption" role="heading">
  <span class="caption-text">
diff --git a/doc/LectureNotes/_build/html/intro.html b/doc/LectureNotes/_build/html/intro.html
index fb6969a76..1f023873a 100644
--- a/doc/LectureNotes/_build/html/intro.html
+++ b/doc/LectureNotes/_build/html/intro.html
@@ -360,6 +360,11 @@ const thebe_selector_output = ".output, .cell_output"
    Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods   and Summary of Course
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek47.html">
+   Exercise week 47
+  </a>
+ </li>
 </ul>
 <p aria-level="2" class="caption" role="heading">
  <span class="caption-text">
diff --git a/doc/LectureNotes/_build/html/objects.inv b/doc/LectureNotes/_build/html/objects.inv
index 3d7768553..88abc5e69 100644
Binary files a/doc/LectureNotes/_build/html/objects.inv and b/doc/LectureNotes/_build/html/objects.inv differ
diff --git a/doc/LectureNotes/_build/html/search.html b/doc/LectureNotes/_build/html/search.html
index 1a813de15..fcc417d15 100644
--- a/doc/LectureNotes/_build/html/search.html
+++ b/doc/LectureNotes/_build/html/search.html
@@ -365,6 +365,11 @@ const thebe_selector_output = ".output, .cell_output"
    Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods   and Summary of Course
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek47.html">
+   Exercise week 47
+  </a>
+ </li>
 </ul>
 <p aria-level="2" class="caption" role="heading">
  <span class="caption-text">
diff --git a/doc/LectureNotes/_build/html/searchindex.js b/doc/LectureNotes/_build/html/searchindex.js
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class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning","<span class=\"section-number\">16. </span>Convolutional Neural Networks","<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view","<span class=\"section-number\">4. </span>Ridge and Lasso Regression","<span class=\"section-number\">5. </span>Resampling Methods","<span class=\"section-number\">6. </span>Logistic Regression","<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims","<span class=\"section-number\">9. </span>Decision trees, overarching aims","<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)","<span class=\"section-number\">13. </span>Neural networks","<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm","<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Exercises week 39","Exercises week 41","Exercises week 42","Exercises weeks 43 and 44","Applied Data Analysis and Machine Learning","<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 9 (midnight), 2023","Project 2 on Machine Learning, deadline November 17 (Midnight)","Project 3 on Machine Learning, deadline December 18 (midnight), 2023","Teaching schedule with links to material","<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Statistical interpretation of Linear Regression and Resampling techniques","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and  Gradient Methods","Week 40: Gradient descent methods (continued) and start Neural networks","Week 41 Neural networks and constructing a neural network code","Week 42 Constructing a Neural Network code with introduction to Tensor flow","Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations","Week 44,  Convolutional Neural Networks (CNN)","Week 45,  Recurrent Neural Networks","Week 46: Decision Trees, Ensemble methods  and Random Forests","Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods   and Summary of 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class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning","<span class=\"section-number\">16. </span>Convolutional Neural Networks","<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view","<span class=\"section-number\">4. </span>Ridge and Lasso Regression","<span class=\"section-number\">5. </span>Resampling Methods","<span class=\"section-number\">6. </span>Logistic Regression","<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims","<span class=\"section-number\">9. </span>Decision trees, overarching aims","<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)","<span class=\"section-number\">13. </span>Neural networks","<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm","<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Exercises week 39","Exercises week 41","Exercises week 42","Exercises weeks 43 and 44","Exercise week 47","Applied Data Analysis and Machine Learning","<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 9 (midnight), 2023","Project 2 on Machine Learning, deadline November 17 (Midnight)","Project 3 on Machine Learning, deadline December 18 (midnight), 2023","Teaching schedule with links to material","<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Statistical interpretation of Linear Regression and Resampling techniques","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and  Gradient Methods","Week 40: Gradient descent methods (continued) and start Neural networks","Week 41 Neural networks and constructing a neural network code","Week 42 Constructing a Neural Network code with introduction to Tensor flow","Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations","Week 44,  Convolutional Neural Networks (CNN)","Week 45,  Recurrent Neural Networks","Week 46: Decision Trees, Ensemble methods  and Random Forests","Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods   and Summary of 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+   "source": [
+    "<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)\n",
+    "doconce format html exercisesweek47.do.txt  -->\n",
+    "<!-- dom:TITLE: Exercise week 47 -->"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4f3fe3f",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "# Exercise week 47\n",
+    "**November 20-24, 2023**\n",
+    "\n",
+    "Date: **Deadline is Sunday November 26 at midnight**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1f597b7",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "# Overarching aims of the exercises this week\n",
+    "\n",
+    "The exercise this week is a simple course survey and feedback.  This\n",
+    "is important for us in order to improve our teaching material, the\n",
+    "active learning format and anything else related to a succesful\n",
+    "mastering of central machine learning methods and their applications."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7497591b",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Why did you choose this course?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c08143f",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### What was your programming knowledge before you started?\n",
+    "\n",
+    "And do you feel this course added to your programming competences and skills?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9783d8a7",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### How do you judge your own level of knowledge on machine learning before and after this course?\n",
+    "\n",
+    "Here you can discuss your level of skill/knowledge at start of course\n",
+    "and at the end of the course and how these matched the level of\n",
+    "skill/knowledge needed to complete the projects."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b0d5c47",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Did the projects and the teaching material allow you to deepen your insights about Machine Learning?\n",
+    "\n",
+    "Feel free to comment here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ead8d47",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Project based teaching and active learning\n",
+    "\n",
+    "This is a project based course and we as teachers would like to keep\n",
+    "it as it is since we see very clearly that people who attend this\n",
+    "course have a very good learning outcome. Project based courses are\n",
+    "however demanding (and expensive seen from the university admin) when\n",
+    "it comes to proper feedback and evaluations. Feel free to discuss\n",
+    "whether you found a project-based and active learning approach\n",
+    "useful. Feel also free to comment upon things we can improve upon or\n",
+    "alternative ways to assess whether the learning outcomes have been\n",
+    "achieved. Would you for example prefer a standard 4 hours written exam\n",
+    "be something you would prefer?  Or other alternatives to projects? We\n",
+    "would very much value your thoughts here since projects are an\n",
+    "essential part of this course."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "746bd238",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Usefulness of the weekly exercises\n",
+    "\n",
+    "Did the weekly exercises help in getting started with the projects?\n",
+    "How relevant where they for solving the projects?  Feel free to\n",
+    "elaborate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ce96822",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Active learning/lab sessions and lectures\n",
+    "\n",
+    "Was there a good link between lectures and active learning sessions?\n",
+    "Would you prefer an active learning environment only with no lectures\n",
+    "or would you prefer a more lecture based format with lab sessions only\n",
+    "(that is no discussion at the beginning of the lab sessions)? Feel\n",
+    "free to comment."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5dc0637a",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### How would you improve this course?\n",
+    "\n",
+    "Are there topics which are missing, topics which could have been\n",
+    "omitted and/or discussed in more depth? Feel free to add your comments\n",
+    "here such as how to improve to teaching material and more."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a98adf6c",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Then some basic questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a935279",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Which is your preferred information chanel, Canvas, Discord, mail or other?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41592552",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Was the weekly update with plans etc useful?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e79a2360",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Was it easy to access the course material?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8bc4dfc9",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Which resources and tools did you use? Jupyter-notebooks, GitHub, the various textbooks we have recommended, etc etc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf415075",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### If you did not attend the lectures or the active learning/lab sessions, which resources did you use?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db3db20a",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Any other topics, impressions, ideas etc you would like to share with us?"
+   ]
+  }
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek47.txt b/doc/LectureNotes/_build/jupyter_execute/exercisesweek47.txt
new file mode 100644
index 000000000..e69de29bb
diff --git a/doc/LectureNotes/_toc.yml b/doc/LectureNotes/_toc.yml
index 5e61d7acf..b900cf0be 100644
--- a/doc/LectureNotes/_toc.yml
+++ b/doc/LectureNotes/_toc.yml
@@ -64,6 +64,7 @@ parts:
     - file: week45.ipynb
     - file: week46.ipynb
     - file: week47.ipynb
+    - file: exercisesweek47.ipynb
   - caption: Projects
     numbered: false
     chapters:
diff --git a/doc/LectureNotes/exercisesweek47.ipynb b/doc/LectureNotes/exercisesweek47.ipynb
new file mode 100644
index 000000000..2e2758065
--- /dev/null
+++ b/doc/LectureNotes/exercisesweek47.ipynb
@@ -0,0 +1,232 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "29616fbf",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)\n",
+    "doconce format html exercisesweek47.do.txt  -->\n",
+    "<!-- dom:TITLE: Exercise week 47 -->"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4f3fe3f",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "# Exercise week 47\n",
+    "**November 20-24, 2023**\n",
+    "\n",
+    "Date: **Deadline is Sunday November 26 at midnight**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1f597b7",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "# Overarching aims of the exercises this week\n",
+    "\n",
+    "The exercise this week is a simple course survey and feedback.  This\n",
+    "is important for us in order to improve our teaching material, the\n",
+    "active learning format and anything else related to a succesful\n",
+    "mastering of central machine learning methods and their applications."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7497591b",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Why did you choose this course?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c08143f",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### What was your programming knowledge before you started?\n",
+    "\n",
+    "And do you feel this course added to your programming competences and skills?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9783d8a7",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### How do you judge your own level of knowledge on machine learning before and after this course?\n",
+    "\n",
+    "Here you can discuss your level of skill/knowledge at start of course\n",
+    "and at the end of the course and how these matched the level of\n",
+    "skill/knowledge needed to complete the projects."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b0d5c47",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Did the projects and the teaching material allow you to deepen your insights about Machine Learning?\n",
+    "\n",
+    "Feel free to comment here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ead8d47",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Project based teaching and active learning\n",
+    "\n",
+    "This is a project based course and we as teachers would like to keep\n",
+    "it as it is since we see very clearly that people who attend this\n",
+    "course have a very good learning outcome. Project based courses are\n",
+    "however demanding (and expensive seen from the university admin) when\n",
+    "it comes to proper feedback and evaluations. Feel free to discuss\n",
+    "whether you found a project-based and active learning approach\n",
+    "useful. Feel also free to comment upon things we can improve upon or\n",
+    "alternative ways to assess whether the learning outcomes have been\n",
+    "achieved. Would you for example prefer a standard 4 hours written exam\n",
+    "be something you would prefer?  Or other alternatives to projects? We\n",
+    "would very much value your thoughts here since projects are an\n",
+    "essential part of this course."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "746bd238",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Usefulness of the weekly exercises\n",
+    "\n",
+    "Did the weekly exercises help in getting started with the projects?\n",
+    "How relevant where they for solving the projects?  Feel free to\n",
+    "elaborate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ce96822",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Active learning/lab sessions and lectures\n",
+    "\n",
+    "Was there a good link between lectures and active learning sessions?\n",
+    "Would you prefer an active learning environment only with no lectures\n",
+    "or would you prefer a more lecture based format with lab sessions only\n",
+    "(that is no discussion at the beginning of the lab sessions)? Feel\n",
+    "free to comment."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5dc0637a",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### How would you improve this course?\n",
+    "\n",
+    "Are there topics which are missing, topics which could have been\n",
+    "omitted and/or discussed in more depth? Feel free to add your comments\n",
+    "here such as how to improve to teaching material and more."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a98adf6c",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Then some basic questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a935279",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Which is your preferred information chanel, Canvas, Discord, mail or other?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41592552",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Was the weekly update with plans etc useful?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e79a2360",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Was it easy to access the course material?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8bc4dfc9",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Which resources and tools did you use? Jupyter-notebooks, GitHub, the various textbooks we have recommended, etc etc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf415075",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### If you did not attend the lectures or the active learning/lab sessions, which resources did you use?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db3db20a",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "### Any other topics, impressions, ideas etc you would like to share with us?"
+   ]
+  }
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/src/week47/exercisesweek47.do.txt b/doc/src/week47/exercisesweek47.do.txt
index d6d1caa00..9760286fd 100644
--- a/doc/src/week47/exercisesweek47.do.txt
+++ b/doc/src/week47/exercisesweek47.do.txt
@@ -5,5 +5,80 @@ DATE: Deadline is Sunday November 26 at midnight
 
 =======  Overarching aims of the exercises this week =======
 
-The exercise this week is a simple course survey and feedback.
-This is important for us in order to improve our teaching material, the active learning format and anything else related to a succesful mastering of central machine learning methods and their applications.
+The exercise this week is a simple course survey and feedback.  This
+is important for us in order to improve our teaching material, the
+active learning format and anything else related to a succesful
+mastering of central machine learning methods and their applications.
+
+
+=== Why did you choose this course? ===
+
+=== What was your programming knowledge before you started? ===
+And do you feel this course added to your programming competences and skills?
+
+=== How do you judge your own level of knowledge on machine learning before and after this course? ===
+
+Here you can discuss your level of skill/knowledge at start of course
+and at the end of the course and how these matched the level of
+skill/knowledge needed to complete the projects.
+
+=== Did the projects and the teaching material allow you to deepen your insights about Machine Learning? ===
+
+Feel free to comment here. 
+
+
+=== Project based teaching and active learning ===
+
+This is a project based course and we as teachers would like to keep
+it as it is since we see very clearly that people who attend this
+course have a very good learning outcome. Project based courses are
+however demanding (and expensive seen from the university admin) when
+it comes to proper feedback and evaluations. Feel free to discuss
+whether you found a project-based and active learning approach
+useful. Feel also free to comment upon things we can improve upon or
+alternative ways to assess whether the learning outcomes have been
+achieved. Would you for example prefer a standard 4 hours written exam
+be something you would prefer?  Or other alternatives to projects? We
+would very much value your thoughts here since projects are an
+essential part of this course.
+
+=== Usefulness of the weekly exercises ===
+
+Did the weekly exercises help in getting started with the projects?
+How relevant where they for solving the projects?  Feel free to
+elaborate
+
+=== Active learning/lab sessions and lectures ===
+
+Was there a good link between lectures and active learning sessions?
+Would you prefer an active learning environment only with no lectures
+or would you prefer a more lecture based format with lab sessions only
+(that is no discussion at the beginning of the lab sessions)? Feel
+free to comment.
+
+
+
+=== How would you improve this course? ===
+
+Are there topics which are missing, topics which could have been
+omitted and/or discussed in more depth? Feel free to add your comments
+here such as how to improve to teaching material and more.
+
+
+
+===== Then some basic questions =====
+
+=== Which is your preferred information chanel, Canvas, Discord, mail or other?  ===
+
+=== Was the weekly update with plans etc useful? ===
+
+=== Was it easy to access the course material? ===
+
+=== Which resources and tools did you use? Jupyter-notebooks, GitHub, the various textbooks we have recommended, etc etc ===
+
+=== If you did not attend the lectures or the active learning/lab sessions, which resources did you use? ===
+
+
+=== Any other topics, impressions, ideas etc you would like to share with us? ===
+
+