From af7737872cb2a5bede490afd494002e4df33ee7f Mon Sep 17 00:00:00 2001
From: Morten Hjorth-Jensen <morten.hjorth-jensen@fys.uio.no>
Date: Sat, 23 Nov 2024 22:44:33 +0100
Subject: [PATCH] typos

---
 doc/pub/week48/html/week48-reveal.html       |  61 ++--
 doc/pub/week48/html/week48-solarized.html    |  52 ++-
 doc/pub/week48/html/week48.html              |  52 ++-
 doc/pub/week48/ipynb/ipynb-week48-src.tar.gz | Bin 823660 -> 823660 bytes
 doc/pub/week48/ipynb/week48.ipynb            | 357 +++++++++----------
 doc/src/week48/week48.do.txt                 |  52 ++-
 6 files changed, 271 insertions(+), 303 deletions(-)

diff --git a/doc/pub/week48/html/week48-reveal.html b/doc/pub/week48/html/week48-reveal.html
index f77f43024..1ceb2e043 100644
--- a/doc/pub/week48/html/week48-reveal.html
+++ b/doc/pub/week48/html/week48-reveal.html
@@ -243,7 +243,7 @@ MathJax.Hub.Config({
 <p>The algorithm described here can be applied to both classification and regression problems.</p>
 
 <p>We will grow of forest of say \( B \) trees.</p>
-<ol>
+<ul>
 <p><li> For \( b=1:B \)
 <ol type="a"></li>
  <p><li> Draw a bootstrap sample from the training data organized in our \( \boldsymbol{X} \) matrix.</li>
@@ -259,8 +259,9 @@ MathJax.Hub.Config({
 <p>
 </ol>
 <p>
-<p><li> Output then the ensemble of trees \( \{T_b\}_1^{B} \) and make predictions for either a regression type of problem or a classification type of problem.</li> 
-</ol>
+</ul>
+<p>
+<p>Finally we output then the ensemble of trees \( \{T_b\}_1^{B} \) and make predictions for either a regression type of problem or a classification type of problem. </p>
 </section>
 
 <section>
@@ -1361,8 +1362,7 @@ plt.show()
 <section>
 <h2 id="machine-learning">Machine learning </h2>
 
-<p>The following topics will be covered</p>
-<ol>
+<ul>
 <p><li> Linear methods for regression and classification:
 <ol type="a"></li>
  <p><li> Ordinary Least Squares</li>
@@ -1393,7 +1393,7 @@ plt.show()
  <p><li> Regression</li>
 </ol>
 <p>
-</ol>
+</ul>
 </section>
 
 <section>
@@ -1468,17 +1468,17 @@ Neural Networks, etc.</a>
 <section>
 <h2 id="choose-a-model-and-algorithm">Choose a Model and Algorithm  </h2>
 
-<ol>
+<ul>
 <p><li> Supervised?</li>
 <p><li> Start with the simplest model that fits your problem</li>
 <p><li> Start with minimal processing of data</li>
-</ol>
+</ul>
 </section>
 
 <section>
 <h2 id="preparing-your-data">Preparing Your Data </h2>
 
-<ol>
+<ul>
 <p><li> Shuffle your data</li>
 <p><li> Mean center your data</li>
 <ul>
@@ -1500,51 +1500,38 @@ Neural Networks, etc.</a>
 <p><li> Can be hit or miss</li>
 </ul>
 <p>
-<p><li> When to do train/test split?</li>
-</ol>
+ <p><li> When to do train/test split?</li>
+</ul>
 </section>
 
 <section>
 <h2 id="which-activation-and-weights-to-choose-in-neural-networks">Which activation and weights to choose in neural networks </h2>
 
-<ol>
+<ul>
 <p><li> RELU? ELU? GELU? etc</li>
 <p><li> Sigmoid or Tanh?</li>
-<p><li> Set all weights to 0?</li>
-<ul>
-
-<p><li> Terrible idea</li>
+<p><li> Set all weights to 0? Terrible idea</li>
+<p><li> Set all weights to random values? Small random values</li>
 </ul>
-<p>
-<p><li> Set all weights to random values?</li>
-<ul>
-
-<p><li> Small random values</li>
-</ul>
-<p>
-</ol>
 </section>
 
 <section>
 <h2 id="optimization-methods-and-hyperparameters">Optimization Methods and Hyperparameters </h2>
-<ol>
-<p><li> Stochastic gradient descent
-<ol type="a"></li>
-<p><li> Stochastic gradient descent + momentum</li>
-</ol>
-<p>
-<p><li> State-of-the-art approaches:</li>
 <ul>
-
-<p><li> RMSProp</li>
-
-<p><li> Adam</li>
-
-<p><li> and more</li>
+<p><li> Stochastic gradient descent</li>
+<ul>
+ <p><li> Stochastic gradient descent + momentum</li>
 </ul>
 <p>
+<p><li> State-of-the-art approaches:
+<ol type="a"></li>
+ <p><li> RMSProp</li>
+ <p><li> Adam</li>
+ <p><li> and more</li>
 </ol>
 <p>
+</ul>
+<p>
 <p>Which regularization and hyperparameters? \( L_1 \) or \( L_2 \), soft
 classifiers, depths of trees and many other. Need to explore a large
 set of hyperparameters and regularization methods.
diff --git a/doc/pub/week48/html/week48-solarized.html b/doc/pub/week48/html/week48-solarized.html
index bff9c030e..18f9c74c8 100644
--- a/doc/pub/week48/html/week48-solarized.html
+++ b/doc/pub/week48/html/week48-solarized.html
@@ -353,7 +353,7 @@ MathJax.Hub.Config({
 <p>The algorithm described here can be applied to both classification and regression problems.</p>
 
 <p>We will grow of forest of say \( B \) trees.</p>
-<ol>
+<ul>
 <li> For \( b=1:B \)
 <ol type="a"></li>
  <li> Draw a bootstrap sample from the training data organized in our \( \boldsymbol{X} \) matrix.</li>
@@ -364,8 +364,9 @@ MathJax.Hub.Config({
    <li> split the node into daughter nodes</li>
 </ol>
 </ol>
-<li> Output then the ensemble of trees \( \{T_b\}_1^{B} \) and make predictions for either a regression type of problem or a classification type of problem.</li> 
-</ol>
+</ul>
+<p>Finally we output then the ensemble of trees \( \{T_b\}_1^{B} \) and make predictions for either a regression type of problem or a classification type of problem. </p>
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="random-forests-compared-with-other-methods-on-the-cancer-data">Random Forests Compared with other Methods on the Cancer Data </h2>
 
@@ -1377,8 +1378,7 @@ plt.show()
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="machine-learning">Machine learning </h2>
 
-<p>The following topics will be covered</p>
-<ol>
+<ul>
 <li> Linear methods for regression and classification:
 <ol type="a"></li>
  <li> Ordinary Least Squares</li>
@@ -1405,7 +1405,7 @@ plt.show()
  <li> Kernel methods</li>
  <li> Regression</li>
 </ol>
-</ol>
+</ul>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="learning-outcomes-and-overarching-aims-of-this-course">Learning outcomes and overarching aims of this course </h2>
 
@@ -1470,15 +1470,15 @@ Neural Networks, etc.</a>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="choose-a-model-and-algorithm">Choose a Model and Algorithm  </h2>
 
-<ol>
+<ul>
 <li> Supervised?</li>
 <li> Start with the simplest model that fits your problem</li>
 <li> Start with minimal processing of data</li>
-</ol>
+</ul>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="preparing-your-data">Preparing Your Data </h2>
 
-<ol>
+<ul>
 <li> Shuffle your data</li>
 <li> Mean center your data</li>
 <ul>
@@ -1493,37 +1493,31 @@ Neural Networks, etc.</a>
   <li> Decorrelates data</li>
   <li> Can be hit or miss</li>
 </ul>
-<li> When to do train/test split?</li>
-</ol>
+ <li> When to do train/test split?</li>
+</ul>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="which-activation-and-weights-to-choose-in-neural-networks">Which activation and weights to choose in neural networks </h2>
 
-<ol>
+<ul>
 <li> RELU? ELU? GELU? etc</li>
 <li> Sigmoid or Tanh?</li>
-<li> Set all weights to 0?</li>
-<ul>
-  <li> Terrible idea</li>
+<li> Set all weights to 0? Terrible idea</li>
+<li> Set all weights to random values? Small random values</li>
 </ul>
-<li> Set all weights to random values?</li>
-<ul>
-  <li> Small random values</li>
-</ul>
-</ol>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="optimization-methods-and-hyperparameters">Optimization Methods and Hyperparameters </h2>
-<ol>
-<li> Stochastic gradient descent
-<ol type="a"></li>
-<li> Stochastic gradient descent + momentum</li>
-</ol>
-<li> State-of-the-art approaches:</li>
 <ul>
-  <li> RMSProp</li>
-  <li> Adam</li>
-  <li> and more</li>
+<li> Stochastic gradient descent</li>
+<ul>
+ <li> Stochastic gradient descent + momentum</li>
 </ul>
+<li> State-of-the-art approaches:
+<ol type="a"></li>
+ <li> RMSProp</li>
+ <li> Adam</li>
+ <li> and more</li>
 </ol>
+</ul>
 <p>Which regularization and hyperparameters? \( L_1 \) or \( L_2 \), soft
 classifiers, depths of trees and many other. Need to explore a large
 set of hyperparameters and regularization methods.
diff --git a/doc/pub/week48/html/week48.html b/doc/pub/week48/html/week48.html
index 010721e6d..a0d63e6fe 100644
--- a/doc/pub/week48/html/week48.html
+++ b/doc/pub/week48/html/week48.html
@@ -430,7 +430,7 @@ MathJax.Hub.Config({
 <p>The algorithm described here can be applied to both classification and regression problems.</p>
 
 <p>We will grow of forest of say \( B \) trees.</p>
-<ol>
+<ul>
 <li> For \( b=1:B \)
 <ol type="a"></li>
  <li> Draw a bootstrap sample from the training data organized in our \( \boldsymbol{X} \) matrix.</li>
@@ -441,8 +441,9 @@ MathJax.Hub.Config({
    <li> split the node into daughter nodes</li>
 </ol>
 </ol>
-<li> Output then the ensemble of trees \( \{T_b\}_1^{B} \) and make predictions for either a regression type of problem or a classification type of problem.</li> 
-</ol>
+</ul>
+<p>Finally we output then the ensemble of trees \( \{T_b\}_1^{B} \) and make predictions for either a regression type of problem or a classification type of problem. </p>
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="random-forests-compared-with-other-methods-on-the-cancer-data">Random Forests Compared with other Methods on the Cancer Data </h2>
 
@@ -1454,8 +1455,7 @@ plt<span style="color: #666666">.</span>show()
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="machine-learning">Machine learning </h2>
 
-<p>The following topics will be covered</p>
-<ol>
+<ul>
 <li> Linear methods for regression and classification:
 <ol type="a"></li>
  <li> Ordinary Least Squares</li>
@@ -1482,7 +1482,7 @@ plt<span style="color: #666666">.</span>show()
  <li> Kernel methods</li>
  <li> Regression</li>
 </ol>
-</ol>
+</ul>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="learning-outcomes-and-overarching-aims-of-this-course">Learning outcomes and overarching aims of this course </h2>
 
@@ -1547,15 +1547,15 @@ Neural Networks, etc.</a>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="choose-a-model-and-algorithm">Choose a Model and Algorithm  </h2>
 
-<ol>
+<ul>
 <li> Supervised?</li>
 <li> Start with the simplest model that fits your problem</li>
 <li> Start with minimal processing of data</li>
-</ol>
+</ul>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="preparing-your-data">Preparing Your Data </h2>
 
-<ol>
+<ul>
 <li> Shuffle your data</li>
 <li> Mean center your data</li>
 <ul>
@@ -1570,37 +1570,31 @@ Neural Networks, etc.</a>
   <li> Decorrelates data</li>
   <li> Can be hit or miss</li>
 </ul>
-<li> When to do train/test split?</li>
-</ol>
+ <li> When to do train/test split?</li>
+</ul>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="which-activation-and-weights-to-choose-in-neural-networks">Which activation and weights to choose in neural networks </h2>
 
-<ol>
+<ul>
 <li> RELU? ELU? GELU? etc</li>
 <li> Sigmoid or Tanh?</li>
-<li> Set all weights to 0?</li>
-<ul>
-  <li> Terrible idea</li>
+<li> Set all weights to 0? Terrible idea</li>
+<li> Set all weights to random values? Small random values</li>
 </ul>
-<li> Set all weights to random values?</li>
-<ul>
-  <li> Small random values</li>
-</ul>
-</ol>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="optimization-methods-and-hyperparameters">Optimization Methods and Hyperparameters </h2>
-<ol>
-<li> Stochastic gradient descent
-<ol type="a"></li>
-<li> Stochastic gradient descent + momentum</li>
-</ol>
-<li> State-of-the-art approaches:</li>
 <ul>
-  <li> RMSProp</li>
-  <li> Adam</li>
-  <li> and more</li>
+<li> Stochastic gradient descent</li>
+<ul>
+ <li> Stochastic gradient descent + momentum</li>
 </ul>
+<li> State-of-the-art approaches:
+<ol type="a"></li>
+ <li> RMSProp</li>
+ <li> Adam</li>
+ <li> and more</li>
 </ol>
+</ul>
 <p>Which regularization and hyperparameters? \( L_1 \) or \( L_2 \), soft
 classifiers, depths of trees and many other. Need to explore a large
 set of hyperparameters and regularization methods.
diff --git a/doc/pub/week48/ipynb/ipynb-week48-src.tar.gz b/doc/pub/week48/ipynb/ipynb-week48-src.tar.gz
index ba9af317afb7adf07e95743b00eaf6d0ba2f8984..d6def3c343811dc416a2fbce45749cd6eb41f6e2 100644
GIT binary patch
delta 53
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delta 53
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diff --git a/doc/pub/week48/ipynb/week48.ipynb b/doc/pub/week48/ipynb/week48.ipynb
index f289f46c8..486829beb 100644
--- a/doc/pub/week48/ipynb/week48.ipynb
+++ b/doc/pub/week48/ipynb/week48.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "fe7fd644",
+   "id": "3b64c66e",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c407135f",
+   "id": "45ba1703",
    "metadata": {
     "editable": true
    },
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1894285d",
+   "id": "7258309e",
    "metadata": {
     "editable": true
    },
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bceac1c4",
+   "id": "8a4ef0a3",
    "metadata": {
     "editable": true
    },
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eaaa2de8",
+   "id": "e01d37bc",
    "metadata": {
     "editable": true
    },
@@ -95,7 +95,7 @@
     "The algorithm described here can be applied to both classification and regression problems.\n",
     "\n",
     "We will grow of forest of say $B$ trees.\n",
-    "1. For $b=1:B$\n",
+    "* For $b=1:B$\n",
     "\n",
     "a. Draw a bootstrap sample from the training data organized in our $\\boldsymbol{X}$ matrix.\n",
     "\n",
@@ -107,12 +107,12 @@
     "\n",
     "3. split the node into daughter nodes\n",
     "\n",
-    "4. Output then the ensemble of trees $\\{T_b\\}_1^{B}$ and make predictions for either a regression type of problem or a classification type of problem."
+    "Finally we output then the ensemble of trees $\\{T_b\\}_1^{B}$ and make predictions for either a regression type of problem or a classification type of problem."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "36809e3f",
+   "id": "1160253d",
    "metadata": {
     "editable": true
    },
@@ -123,7 +123,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "ef24e54c",
+   "id": "ac77382f",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -197,7 +197,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0472d908",
+   "id": "254c4495",
    "metadata": {
     "editable": true
    },
@@ -213,7 +213,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "08e636dd",
+   "id": "5633e7a9",
    "metadata": {
     "editable": true
    },
@@ -224,7 +224,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "90e2c43c",
+   "id": "250bca6d",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -239,7 +239,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "929962a3",
+   "id": "a8506102",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -257,7 +257,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a2039540",
+   "id": "8713c888",
    "metadata": {
     "editable": true
    },
@@ -277,7 +277,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "140e30ce",
+   "id": "efc5e7e7",
    "metadata": {
     "editable": true
    },
@@ -291,7 +291,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "02cb3a63",
+   "id": "ee68d9b3",
    "metadata": {
     "editable": true
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@@ -303,7 +303,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9924d331",
+   "id": "6efcd942",
    "metadata": {
     "editable": true
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@@ -320,7 +320,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6e00565f",
+   "id": "8b2a4e15",
    "metadata": {
     "editable": true
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@@ -332,7 +332,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de290cb8",
+   "id": "77d4f75d",
    "metadata": {
     "editable": true
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@@ -346,7 +346,7 @@
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   {
    "cell_type": "markdown",
-   "id": "fd4fac8f",
+   "id": "9d0680d2",
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     "editable": true
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@@ -358,7 +358,7 @@
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   {
    "cell_type": "markdown",
-   "id": "1ddfce8e",
+   "id": "a5f51b6a",
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@@ -371,7 +371,7 @@
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   {
    "cell_type": "markdown",
-   "id": "9e18b5ba",
+   "id": "c6eafc82",
    "metadata": {
     "editable": true
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@@ -383,7 +383,7 @@
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   {
    "cell_type": "markdown",
-   "id": "52663df1",
+   "id": "d42a3420",
    "metadata": {
     "editable": true
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@@ -393,7 +393,7 @@
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   {
    "cell_type": "markdown",
-   "id": "a6bfc5a8",
+   "id": "ecac5a19",
    "metadata": {
     "editable": true
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@@ -421,7 +421,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bd232678",
+   "id": "632d7379",
    "metadata": {
     "editable": true
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@@ -437,7 +437,7 @@
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   {
    "cell_type": "markdown",
-   "id": "8f945bbd",
+   "id": "3a2dc7cd",
    "metadata": {
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@@ -449,7 +449,7 @@
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   {
    "cell_type": "markdown",
-   "id": "14894a48",
+   "id": "d1dae1f7",
    "metadata": {
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@@ -460,7 +460,7 @@
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   {
    "cell_type": "markdown",
-   "id": "31d3d404",
+   "id": "db8e52c8",
    "metadata": {
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@@ -472,7 +472,7 @@
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   {
    "cell_type": "markdown",
-   "id": "0d86e417",
+   "id": "b71e4562",
    "metadata": {
     "editable": true
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@@ -482,7 +482,7 @@
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   {
    "cell_type": "markdown",
-   "id": "dba5d81b",
+   "id": "8b287320",
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@@ -494,7 +494,7 @@
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   {
    "cell_type": "markdown",
-   "id": "9946eed4",
+   "id": "444b31c7",
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     "editable": true
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@@ -504,7 +504,7 @@
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   {
    "cell_type": "markdown",
-   "id": "671b57e9",
+   "id": "975f549b",
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@@ -516,7 +516,7 @@
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   {
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-   "id": "2f1afb73",
+   "id": "49707e43",
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@@ -526,7 +526,7 @@
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   {
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-   "id": "eb06d7bb",
+   "id": "30fff982",
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@@ -538,7 +538,7 @@
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   {
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-   "id": "bd75f76e",
+   "id": "8936f422",
    "metadata": {
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@@ -552,7 +552,7 @@
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    "cell_type": "markdown",
-   "id": "7a2457ae",
+   "id": "373a4a0c",
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@@ -568,7 +568,7 @@
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   {
    "cell_type": "markdown",
-   "id": "88fa4f5c",
+   "id": "c412ab0e",
    "metadata": {
     "editable": true
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@@ -580,7 +580,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e100093d",
+   "id": "0018a744",
    "metadata": {
     "editable": true
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@@ -596,7 +596,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a96c6add",
+   "id": "d092eb4b",
    "metadata": {
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@@ -608,7 +608,7 @@
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   {
    "cell_type": "markdown",
-   "id": "4ee0fb37",
+   "id": "17820826",
    "metadata": {
     "editable": true
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@@ -618,7 +618,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b7206f37",
+   "id": "f4910537",
    "metadata": {
     "editable": true
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@@ -630,7 +630,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca864761",
+   "id": "bfae7a07",
    "metadata": {
     "editable": true
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@@ -642,7 +642,7 @@
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   {
    "cell_type": "markdown",
-   "id": "b8776574",
+   "id": "f00bee3c",
    "metadata": {
     "editable": true
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@@ -654,7 +654,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7afe77b7",
+   "id": "bf267f5b",
    "metadata": {
     "editable": true
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@@ -665,7 +665,7 @@
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   {
    "cell_type": "markdown",
-   "id": "1915c382",
+   "id": "a6eb9909",
    "metadata": {
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@@ -677,7 +677,7 @@
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   {
    "cell_type": "markdown",
-   "id": "57398dbb",
+   "id": "2bbceaad",
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     "editable": true
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@@ -688,7 +688,7 @@
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   {
    "cell_type": "markdown",
-   "id": "b63693b2",
+   "id": "9ff79322",
    "metadata": {
     "editable": true
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@@ -700,7 +700,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09e68eb6",
+   "id": "6c4299e2",
    "metadata": {
     "editable": true
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@@ -710,7 +710,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bf86c857",
+   "id": "068e2930",
    "metadata": {
     "editable": true
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@@ -722,7 +722,7 @@
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   {
    "cell_type": "markdown",
-   "id": "cb3d645c",
+   "id": "53eb174c",
    "metadata": {
     "editable": true
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@@ -734,7 +734,7 @@
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   {
    "cell_type": "markdown",
-   "id": "2157edce",
+   "id": "3baeddb4",
    "metadata": {
     "editable": true
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@@ -746,7 +746,7 @@
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   {
    "cell_type": "markdown",
-   "id": "b9e39b81",
+   "id": "87178662",
    "metadata": {
     "editable": true
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@@ -758,7 +758,7 @@
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   {
    "cell_type": "markdown",
-   "id": "941fa37d",
+   "id": "c48316bd",
    "metadata": {
     "editable": true
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@@ -768,7 +768,7 @@
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   {
    "cell_type": "markdown",
-   "id": "3ce9f347",
+   "id": "6ccbebe4",
    "metadata": {
     "editable": true
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@@ -780,7 +780,7 @@
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   {
    "cell_type": "markdown",
-   "id": "d26d3711",
+   "id": "526ec37c",
    "metadata": {
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@@ -790,7 +790,7 @@
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   {
    "cell_type": "markdown",
-   "id": "44bce843",
+   "id": "84e556c0",
    "metadata": {
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@@ -802,7 +802,7 @@
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   {
    "cell_type": "markdown",
-   "id": "1c997239",
+   "id": "931a10f7",
    "metadata": {
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@@ -812,7 +812,7 @@
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   {
    "cell_type": "markdown",
-   "id": "216bdcdb",
+   "id": "45b79d8b",
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@@ -824,7 +824,7 @@
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   {
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-   "id": "373c405a",
+   "id": "e30b9dee",
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@@ -834,7 +834,7 @@
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   {
    "cell_type": "markdown",
-   "id": "c57bddad",
+   "id": "c8d42197",
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@@ -846,7 +846,7 @@
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   {
    "cell_type": "markdown",
-   "id": "5e936ef6",
+   "id": "56d6acfd",
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@@ -856,7 +856,7 @@
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   {
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-   "id": "2c9eaf83",
+   "id": "df837af9",
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@@ -868,7 +868,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bdecca1e",
+   "id": "1fafddb8",
    "metadata": {
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@@ -888,7 +888,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fea06c34",
+   "id": "8a898c02",
    "metadata": {
     "editable": true
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@@ -900,7 +900,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0025dfdf",
+   "id": "03435f7c",
    "metadata": {
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@@ -910,7 +910,7 @@
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   {
    "cell_type": "markdown",
-   "id": "13f97440",
+   "id": "ac433aeb",
    "metadata": {
     "editable": true
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@@ -926,7 +926,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ee2b62a1",
+   "id": "37e79583",
    "metadata": {
     "editable": true
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@@ -938,7 +938,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "35cc8d6f",
+   "id": "6eff57bb",
    "metadata": {
     "editable": true
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@@ -966,7 +966,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "90ca86cd",
+   "id": "143fddc5",
    "metadata": {
     "editable": true
    },
@@ -979,7 +979,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "d3803033",
+   "id": "627dcde1",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1004,7 +1004,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ac68d8bc",
+   "id": "0586f2f5",
    "metadata": {
     "editable": true
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@@ -1015,7 +1015,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "679b3966",
+   "id": "57f72ee8",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1104,7 +1104,7 @@
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   {
    "cell_type": "markdown",
-   "id": "58adfdb1",
+   "id": "98bcb1fd",
    "metadata": {
     "editable": true
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@@ -1122,7 +1122,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca89c2b5",
+   "id": "adfad730",
    "metadata": {
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@@ -1135,7 +1135,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bee751db",
+   "id": "32e3c207",
    "metadata": {
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@@ -1147,7 +1147,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "64ec846c",
+   "id": "460df33e",
    "metadata": {
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@@ -1157,7 +1157,7 @@
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   {
    "cell_type": "markdown",
-   "id": "2af461db",
+   "id": "ec1e9c33",
    "metadata": {
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@@ -1169,7 +1169,7 @@
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   {
    "cell_type": "markdown",
-   "id": "b6502394",
+   "id": "5f517f3c",
    "metadata": {
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@@ -1179,7 +1179,7 @@
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   {
    "cell_type": "markdown",
-   "id": "48d243dc",
+   "id": "8352e0e4",
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@@ -1191,7 +1191,7 @@
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   {
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-   "id": "e03e872f",
+   "id": "b158a9ff",
    "metadata": {
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@@ -1204,7 +1204,7 @@
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   {
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-   "id": "d1613021",
+   "id": "17fb6c35",
    "metadata": {
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@@ -1216,7 +1216,7 @@
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   {
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-   "id": "d5e7fe64",
+   "id": "64c802de",
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@@ -1228,7 +1228,7 @@
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   {
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-   "id": "0bd5bef5",
+   "id": "f4b660f8",
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@@ -1240,7 +1240,7 @@
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   {
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-   "id": "ef5252a7",
+   "id": "d592d84b",
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@@ -1250,7 +1250,7 @@
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   {
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-   "id": "7cb83769",
+   "id": "265f57f6",
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@@ -1262,7 +1262,7 @@
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   {
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-   "id": "6b0d28d0",
+   "id": "149a5a32",
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@@ -1272,7 +1272,7 @@
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   {
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-   "id": "4762090e",
+   "id": "fb347cb0",
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@@ -1288,7 +1288,7 @@
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   {
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-   "id": "59124920",
+   "id": "66b696bb",
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@@ -1300,7 +1300,7 @@
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   {
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-   "id": "70bba454",
+   "id": "6c776311",
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@@ -1321,7 +1321,7 @@
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   {
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-   "id": "97c28672",
+   "id": "b7cecfb0",
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@@ -1332,7 +1332,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "380a6f12",
+   "id": "7b1eb376",
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@@ -1383,7 +1383,7 @@
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   {
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-   "id": "02a1a349",
+   "id": "9d97b3d7",
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@@ -1394,7 +1394,7 @@
   {
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-   "id": "931c8836",
+   "id": "7e853c55",
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@@ -1442,7 +1442,7 @@
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   {
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-   "id": "ccd968ba",
+   "id": "b458d799",
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@@ -1465,7 +1465,7 @@
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   {
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+   "id": "48d1de5e",
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@@ -1478,7 +1478,7 @@
   {
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-   "id": "b18ceffd",
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@@ -1536,7 +1536,7 @@
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@@ -1547,7 +1547,7 @@
   {
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-   "id": "2bf430ed",
+   "id": "8ab70463",
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@@ -1634,7 +1634,7 @@
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@@ -1644,7 +1644,7 @@
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-   "id": "a67ab616",
+   "id": "780df2c0",
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@@ -1659,7 +1659,7 @@
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   {
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-   "id": "00a62442",
+   "id": "8ead86e5",
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@@ -1675,7 +1675,7 @@
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   {
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-   "id": "483051ba",
+   "id": "31266704",
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@@ -1700,15 +1700,14 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7429c058",
+   "id": "2021a43b",
    "metadata": {
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    },
    "source": [
     "## Machine learning\n",
     "\n",
-    "The following topics will be covered\n",
-    "1. Linear methods for regression and classification:\n",
+    "* Linear methods for regression and classification:\n",
     "\n",
     "a. Ordinary Least Squares\n",
     "\n",
@@ -1718,7 +1717,7 @@
     "\n",
     "d. Logistic regression\n",
     "\n",
-    "5. Neural networks and deep learning:\n",
+    "* Neural networks and deep learning:\n",
     "\n",
     "a. Feed Forward Neural Networks\n",
     "\n",
@@ -1726,7 +1725,7 @@
     "\n",
     "c. Recurrent Neural Networks\n",
     "\n",
-    "4. Decisions trees and ensemble methods:\n",
+    "* Decisions trees and ensemble methods:\n",
     "\n",
     "a. Decision trees\n",
     "\n",
@@ -1736,7 +1735,7 @@
     "\n",
     "d. Boosting and gradient boosting\n",
     "\n",
-    "5. Not discussed this year: Support vector machines\n",
+    "* Not discussed this year: Support vector machines\n",
     "\n",
     "a. Binary classification and multiclass classification\n",
     "\n",
@@ -1747,7 +1746,7 @@
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   {
    "cell_type": "markdown",
-   "id": "3ab2e37a",
+   "id": "170e1be1",
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@@ -1782,7 +1781,7 @@
   },
   {
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-   "id": "b4a54e3f",
+   "id": "07f2b380",
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@@ -1805,7 +1804,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "68e0ad8c",
+   "id": "4408cc2b",
    "metadata": {
     "editable": true
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@@ -1828,7 +1827,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "319b20fd",
+   "id": "13833ad1",
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@@ -1848,89 +1847,85 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8060a087",
+   "id": "690b357b",
    "metadata": {
     "editable": true
    },
    "source": [
     "## Choose a Model and Algorithm\n",
     "\n",
-    "1. Supervised?\n",
+    "* Supervised?\n",
     "\n",
-    "2. Start with the simplest model that fits your problem\n",
+    "* Start with the simplest model that fits your problem\n",
     "\n",
-    "3. Start with minimal processing of data"
+    "* Start with minimal processing of data"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "be25b51b",
+   "id": "a382da4c",
    "metadata": {
     "editable": true
    },
    "source": [
     "## Preparing Your Data\n",
     "\n",
-    "1. Shuffle your data\n",
+    "* Shuffle your data\n",
     "\n",
-    "2. Mean center your data\n",
+    "* Mean center your data\n",
     "\n",
     "  * Why?\n",
     "\n",
-    "3. Normalize the variance\n",
+    "* Normalize the variance\n",
     "\n",
     "  * Why?\n",
     "\n",
-    "4. **Whitening**\n",
+    "* **Whitening**\n",
     "\n",
     "  * Decorrelates data\n",
     "\n",
     "  * Can be hit or miss\n",
     "\n",
-    "5. When to do train/test split?"
+    " * When to do train/test split?"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2c67fffd",
+   "id": "aef19b0c",
    "metadata": {
     "editable": true
    },
    "source": [
     "## Which activation and weights to choose in neural networks\n",
     "\n",
-    "1. RELU? ELU? GELU? etc\n",
+    "* RELU? ELU? GELU? etc\n",
     "\n",
-    "2. Sigmoid or Tanh?\n",
+    "* Sigmoid or Tanh?\n",
     "\n",
-    "3. Set all weights to 0?\n",
+    "* Set all weights to 0? Terrible idea\n",
     "\n",
-    "  * Terrible idea\n",
-    "\n",
-    "4. Set all weights to random values?\n",
-    "\n",
-    "  * Small random values"
+    "* Set all weights to random values? Small random values"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6ab2cdf4",
+   "id": "f301c68e",
    "metadata": {
     "editable": true
    },
    "source": [
     "## Optimization Methods and Hyperparameters\n",
-    "1. Stochastic gradient descent\n",
+    "* Stochastic gradient descent\n",
     "\n",
-    "a. Stochastic gradient descent + momentum\n",
+    " * Stochastic gradient descent + momentum\n",
     "\n",
-    "2. State-of-the-art approaches:\n",
+    "* State-of-the-art approaches:\n",
     "\n",
-    "  * RMSProp\n",
+    "a. RMSProp\n",
     "\n",
-    "  * Adam\n",
+    "b. Adam\n",
     "\n",
-    "  * and more\n",
+    "c. and more\n",
     "\n",
     "Which regularization and hyperparameters? $L_1$ or $L_2$, soft\n",
     "classifiers, depths of trees and many other. Need to explore a large\n",
@@ -1939,7 +1934,7 @@
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   {
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-   "id": "8a5e039f",
+   "id": "7f82dc43",
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@@ -1957,7 +1952,7 @@
   },
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-   "id": "9a3946d1",
+   "id": "7a1117b1",
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@@ -1987,7 +1982,7 @@
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-   "id": "d9e6d355",
+   "id": "90fe2084",
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@@ -2001,7 +1996,7 @@
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-   "id": "7e99ee2d",
+   "id": "bc8ad0a3",
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@@ -2027,7 +2022,7 @@
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+   "id": "bea1957b",
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@@ -2056,7 +2051,7 @@
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-   "id": "f5e8269a",
+   "id": "c1347ce6",
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@@ -2073,7 +2068,7 @@
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-   "id": "6174662e",
+   "id": "5bcb69f0",
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@@ -2095,7 +2090,7 @@
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-   "id": "36165057",
+   "id": "39597386",
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@@ -2113,7 +2108,7 @@
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-   "id": "659b2e21",
+   "id": "24cb4d77",
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@@ -2136,7 +2131,7 @@
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+   "id": "abe0e029",
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@@ -2155,7 +2150,7 @@
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-   "id": "0bc5fe3d",
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@@ -2171,7 +2166,7 @@
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   {
    "cell_type": "markdown",
-   "id": "68a1f231",
+   "id": "a85f3d50",
    "metadata": {
     "editable": true
    },
@@ -2186,7 +2181,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4aa0e3cf",
+   "id": "3b68f9bc",
    "metadata": {
     "editable": true
    },
@@ -2210,7 +2205,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e440589a",
+   "id": "eeb46a9a",
    "metadata": {
     "editable": true
    },
@@ -2222,7 +2217,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dd9c38a0",
+   "id": "911fc5be",
    "metadata": {
     "editable": true
    },
@@ -2240,7 +2235,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5c393934",
+   "id": "44398f76",
    "metadata": {
     "editable": true
    },
@@ -2250,7 +2245,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ab552000",
+   "id": "d22991a2",
    "metadata": {
     "editable": true
    },
@@ -2268,7 +2263,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "878ae3c8",
+   "id": "c645e455",
    "metadata": {
     "editable": true
    },
@@ -2278,7 +2273,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b830eb6d",
+   "id": "99a56dc1",
    "metadata": {
     "editable": true
    },
@@ -2297,7 +2292,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "683b809a",
+   "id": "9a39b491",
    "metadata": {
     "editable": true
    },
@@ -2309,7 +2304,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "adec988a",
+   "id": "286d0ab5",
    "metadata": {
     "editable": true
    },
@@ -2323,7 +2318,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7c370896",
+   "id": "e21e58ac",
    "metadata": {
     "editable": true
    },
@@ -2338,7 +2333,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1b07308c",
+   "id": "d9e99856",
    "metadata": {
     "editable": true
    },
@@ -2356,7 +2351,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0e443612",
+   "id": "da27eff3",
    "metadata": {
     "editable": true
    },
@@ -2370,7 +2365,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "13f2d272",
+   "id": "ad466922",
    "metadata": {
     "editable": true
    },
@@ -2388,7 +2383,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8c9ac9f3",
+   "id": "e3e1a9ee",
    "metadata": {
     "editable": true
    },
@@ -2412,7 +2407,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "610c29b7",
+   "id": "8a463ba1",
    "metadata": {
     "editable": true
    },
@@ -2450,7 +2445,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "096e7470",
+   "id": "8b88bdc9",
    "metadata": {
     "editable": true
    },
@@ -2473,7 +2468,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fbf18ec6",
+   "id": "dcf0e541",
    "metadata": {
     "editable": true
    },
@@ -2509,7 +2504,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "30ca6ef5",
+   "id": "9d1c6243",
    "metadata": {
     "editable": true
    },
@@ -2530,7 +2525,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "99d1df35",
+   "id": "b06e1c4c",
    "metadata": {
     "editable": true
    },
@@ -2552,7 +2547,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9abcc525",
+   "id": "8bd5d6c4",
    "metadata": {
     "editable": true
    },
@@ -2572,7 +2567,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca2124ca",
+   "id": "40c7ca7e",
    "metadata": {
     "editable": true
    },
@@ -2587,7 +2582,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5858078e",
+   "id": "bc167c7d",
    "metadata": {
     "editable": true
    },
@@ -2605,7 +2600,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "174ee0c8",
+   "id": "06e830c9",
    "metadata": {
     "editable": true
    },
@@ -2635,7 +2630,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e2c44b50",
+   "id": "51b7b00d",
    "metadata": {
     "editable": true
    },
@@ -2664,7 +2659,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6f4f1c4b",
+   "id": "265badfe",
    "metadata": {
     "editable": true
    },
@@ -2695,7 +2690,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3b86535f",
+   "id": "28cda775",
    "metadata": {
     "editable": true
    },
@@ -2718,7 +2713,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "51363de6",
+   "id": "eab731bc",
    "metadata": {
     "editable": true
    },
@@ -2736,7 +2731,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e8a5a5bf",
+   "id": "105015b2",
    "metadata": {
     "editable": true
    },
@@ -2759,7 +2754,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0249293e",
+   "id": "ddbe105b",
    "metadata": {
     "editable": true
    },
@@ -2780,7 +2775,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bfcd921f",
+   "id": "3e53243c",
    "metadata": {
     "editable": true
    },
@@ -2796,7 +2791,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "57427c59",
+   "id": "011485c3",
    "metadata": {
     "editable": true
    },
diff --git a/doc/src/week48/week48.do.txt b/doc/src/week48/week48.do.txt
index 1022ae3e2..abdaa7f1f 100644
--- a/doc/src/week48/week48.do.txt
+++ b/doc/src/week48/week48.do.txt
@@ -38,13 +38,14 @@ o Readings and Videos:
 The algorithm described here can be applied to both classification and regression problems.
 
 We will grow of forest of say $B$ trees.
-o For $b=1:B$
+* For $b=1:B$
   o Draw a bootstrap sample from the training data organized in our $\bm{X}$ matrix.
   o We grow then a random forest tree $T_b$ based on the bootstrapped data by repeating the steps outlined till we reach the maximum node size is reached
     o we select $m \le p$ variables at random from the $p$ predictors/features
     o pick the best split point among the $m$ features using for example the CART algorithm and create a new node
     o split the node into daughter nodes
-o Output then the ensemble of trees $\{T_b\}_1^{B}$ and make predictions for either a regression type of problem or a classification type of problem. 
+
+Finally we output then the ensemble of trees $\{T_b\}_1^{B}$ and make predictions for either a regression type of problem or a classification type of problem. 
 
 
 
@@ -925,22 +926,21 @@ o Not discussed: Principal Component Analysis to reduce the number of features.
 !split
 ===== Machine learning =====
 
-The following topics will be covered
-o Linear methods for regression and classification:
+* Linear methods for regression and classification:
   o Ordinary Least Squares
   o Ridge regression
   o Lasso regression
   o Logistic regression
-o Neural networks and deep learning:
+* Neural networks and deep learning:
   o Feed Forward Neural Networks
   o Convolutional Neural Networks
   o Recurrent Neural Networks
-o Decisions trees and ensemble methods:
+* Decisions trees and ensemble methods:
   o Decision trees
   o Bagging and voting
   o Random forests
   o Boosting and gradient boosting
-o Not discussed this year: Support vector machines
+* Not discussed this year: Support vector machines
   o Binary classification and multiclass classification
   o Kernel methods
   o Regression
@@ -1011,43 +1011,41 @@ o Based on your results, feedback loop to earliest possible point
 !split
 ===== Choose a Model and Algorithm  =====
 
-o Supervised?
-o Start with the simplest model that fits your problem
-o Start with minimal processing of data
+* Supervised?
+* Start with the simplest model that fits your problem
+* Start with minimal processing of data
 
 !split
 ===== Preparing Your Data =====
 
-o Shuffle your data
-o Mean center your data
+* Shuffle your data
+* Mean center your data
   * Why?
-o Normalize the variance
+* Normalize the variance
   * Why?
-o _Whitening_
+* _Whitening_
   * Decorrelates data
   * Can be hit or miss
-o  When to do train/test split?
+*  When to do train/test split?
 
 
 !split
 =====  Which activation and weights to choose in neural networks =====
 
-o RELU? ELU? GELU? etc
-o Sigmoid or Tanh?
-o Set all weights to 0?
-  * Terrible idea
-o Set all weights to random values?
-  * Small random values
+* RELU? ELU? GELU? etc
+* Sigmoid or Tanh?
+* Set all weights to 0? Terrible idea
+* Set all weights to random values? Small random values
 
 
 !split
 ===== Optimization Methods and Hyperparameters =====
-o Stochastic gradient descent
-o  Stochastic gradient descent + momentum
-o State-of-the-art approaches:
-  * RMSProp
-  * Adam
-  * and more
+* Stochastic gradient descent
+*  Stochastic gradient descent + momentum
+* State-of-the-art approaches:
+  o RMSProp
+  o Adam
+  o and more
 
 Which regularization and hyperparameters? $L_1$ or $L_2$, soft
 classifiers, depths of trees and many other. Need to explore a large