adding typos

doc/pub/How2ReadData/html/How2ReadData-bs.html

@@ -166,7 +166,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Dec 22, 2019</h4></center> <!-- date -->
+<center><h4>Dec 24, 2019</h4></center> <!-- date -->
 <br>
 <p>
 </div> <!-- end jumbotron -->
@@ -1205,10 +1205,10 @@ Finally, another cost function is the Huber cost function used in robust regress
 It is less sensitive to outliers in data than the squared error cost function.
 A variant for classification is also sometimes used, a quantity we will meet later. 
 $$
-L_{\delta }(a)={\begin{cases}{\frac {1}{2}}{a^{2}}&{\text{for }}|a|\leq \delta ,\\\delta (|a|-{\frac {1}{2}}\delta ),&{\text{otherwise.}}\end{cases}}}.
+H_{\delta}(a)={\begin{cases}{\frac {1}{2}}{a^{2}}&{\text{for }}|a|\leq \delta ,\\\delta (|a|-{\frac {1}{2}}\delta ),&{\text{otherwise.}}\end{cases}}}.
 $$
 
-<p>
+Here \( a=\boldsymbol{y} - \boldsymbol{\tilde{y}} \).
 We will discuss in more
 detail these and other functions in the various lectures.  We conclude this part with another example. Instead of 
 a linear \( x \)-dependence we study now a cubic polynomial and use the polynomial regression analysis tools of scikit-learn.

doc/pub/How2ReadData/html/How2ReadData-reveal.html

@@ -148,7 +148,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>&nbsp;<br>
-<center><h4>Dec 22, 2019</h4></center> <!-- date -->
+<center><h4>Dec 24, 2019</h4></center> <!-- date -->
 <br>
 
 <h2 id="___sec0">Introduction </h2>
@@ -1231,11 +1231,11 @@ It is less sensitive to outliers in data than the squared error cost function.
 A variant for classification is also sometimes used, a quantity we will meet later. 
 <p>&nbsp;<br>
 $$
-L_{\delta }(a)={\begin{cases}{\frac {1}{2}}{a^{2}}&{\text{for }}|a|\leq \delta ,\\\delta (|a|-{\frac {1}{2}}\delta ),&{\text{otherwise.}}\end{cases}}}.
+H_{\delta}(a)={\begin{cases}{\frac {1}{2}}{a^{2}}&{\text{for }}|a|\leq \delta ,\\\delta (|a|-{\frac {1}{2}}\delta ),&{\text{otherwise.}}\end{cases}}}.
 $$
 <p>&nbsp;<br>
 
-<p>
+Here \( a=\boldsymbol{y} - \boldsymbol{\tilde{y}} \).
 We will discuss in more
 detail these and other functions in the various lectures.  We conclude this part with another example. Instead of 
 a linear \( x \)-dependence we study now a cubic polynomial and use the polynomial regression analysis tools of scikit-learn.

doc/pub/How2ReadData/html/How2ReadData-solarized.html

@@ -135,7 +135,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Dec 22, 2019</h4></center> <!-- date -->
+<center><h4>Dec 24, 2019</h4></center> <!-- date -->
 <br>
 
 <h2 id="___sec0">Introduction </h2>
@@ -1165,10 +1165,10 @@ Finally, another cost function is the Huber cost function used in robust regress
 It is less sensitive to outliers in data than the squared error cost function.
 A variant for classification is also sometimes used, a quantity we will meet later. 
 $$
-L_{\delta }(a)={\begin{cases}{\frac {1}{2}}{a^{2}}&{\text{for }}|a|\leq \delta ,\\\delta (|a|-{\frac {1}{2}}\delta ),&{\text{otherwise.}}\end{cases}}}.
+H_{\delta}(a)={\begin{cases}{\frac {1}{2}}{a^{2}}&{\text{for }}|a|\leq \delta ,\\\delta (|a|-{\frac {1}{2}}\delta ),&{\text{otherwise.}}\end{cases}}}.
 $$
 
-<p>
+Here \( a=\boldsymbol{y} - \boldsymbol{\tilde{y}} \).
 We will discuss in more
 detail these and other functions in the various lectures.  We conclude this part with another example. Instead of 
 a linear \( x \)-dependence we study now a cubic polynomial and use the polynomial regression analysis tools of scikit-learn.

doc/pub/How2ReadData/html/How2ReadData.html

@@ -140,7 +140,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Dec 22, 2019</h4></center> <!-- date -->
+<center><h4>Dec 24, 2019</h4></center> <!-- date -->
 <br>
 
 <h2 id="___sec0">Introduction </h2>
@@ -1170,10 +1170,10 @@ Finally, another cost function is the Huber cost function used in robust regress
 It is less sensitive to outliers in data than the squared error cost function.
 A variant for classification is also sometimes used, a quantity we will meet later. 
 $$
-L_{\delta }(a)={\begin{cases}{\frac {1}{2}}{a^{2}}&{\text{for }}|a|\leq \delta ,\\\delta (|a|-{\frac {1}{2}}\delta ),&{\text{otherwise.}}\end{cases}}}.
+H_{\delta}(a)={\begin{cases}{\frac {1}{2}}{a^{2}}&{\text{for }}|a|\leq \delta ,\\\delta (|a|-{\frac {1}{2}}\delta ),&{\text{otherwise.}}\end{cases}}}.
 $$
 
-<p>
+Here \( a=\boldsymbol{y} - \boldsymbol{\tilde{y}} \).
 We will discuss in more
 detail these and other functions in the various lectures.  We conclude this part with another example. Instead of 
 a linear \( x \)-dependence we study now a cubic polynomial and use the polynomial regression analysis tools of scikit-learn.

doc/pub/How2ReadData/ipynb/How2ReadData.ipynb

@@ -10,7 +10,7 @@
     "<!-- Author: -->  \n",
     "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
     "\n",
-    "Date: **Dec 22, 2019**\n",
+    "Date: **Dec 24, 2019**\n",
     "\n",
     "Copyright 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
     "\n",
@@ -1467,7 +1467,7 @@
    "metadata": {},
    "source": [
     "$$\n",
-    "L_{\\delta }(a)={\\begin{cases}{\\frac {1}{2}}{a^{2}}&{\\text{for }}|a|\\leq \\delta ,\\\\\\delta (|a|-{\\frac {1}{2}}\\delta ),&{\\text{otherwise.}}\\end{cases}}}.\n",
+    "H_{\\delta}(a)={\\begin{cases}{\\frac {1}{2}}{a^{2}}&{\\text{for }}|a|\\leq \\delta ,\\\\\\delta (|a|-{\\frac {1}{2}}\\delta ),&{\\text{otherwise.}}\\end{cases}}}.\n",
     "$$"
    ]
   },
@@ -1475,6 +1475,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "Here $a=\\boldsymbol{y} - \\boldsymbol{\\tilde{y}}$.\n",
     "We will discuss in more\n",
     "detail these and other functions in the various lectures.  We conclude this part with another example. Instead of \n",
     "a linear $x$-dependence we study now a cubic polynomial and use the polynomial regression analysis tools of scikit-learn."