correcting typos

doc/pub/week36/html/week36-reveal.html

@@ -670,14 +670,14 @@ Let us assume we have a data set with outputs/targets given by the vector
 
 <p>&nbsp;<br>
 $$
-\boldsymbol{y}=\begin{matrix}4 \\ 2 \\3\end{bmatrix},
+\boldsymbol{y}=\begin{bmatrix}4 \\ 2 \\3\end{bmatrix},
 $$
 <p>&nbsp;<br>
 
 and our inputs as a \( 3\times 2 \) design matrix
 <p>&nbsp;<br>
 $$
-\boldsymbol{X}=\begin{matrix}2 & 0\\ 0 & 1 \\ 1 & 0\end{bmatrix},
+\boldsymbol{X}=\begin{bmatrix}2 & 0\\ 0 & 1 \\ 1 & 0\end{bmatrix},
 $$
 <p>&nbsp;<br>
 
@@ -701,7 +701,7 @@ Inserting the above values we obtain that
 
 <p>&nbsp;<br>
 $$
-\hat{\boldsymbol{\beta}}^{\mathrm{OLS}}=\begin{matrix}\frac{11}{5} \\ 2\end{bmatrix},
+\hat{\boldsymbol{\beta}}^{\mathrm{OLS}}=\begin{bmatrix}\frac{11}{5} \\ 2\end{bmatrix},
 $$
 <p>&nbsp;<br>
 
@@ -729,7 +729,7 @@ Inserting the above values we obtain that
 
 <p>&nbsp;<br>
 $$
-\hat{\boldsymbol{\beta}}^{\mathrm{Ridge}}=\begin{matrix}\frac{11}{5+\lambda} \\ \frac{2}{1+\lambda}\end{bmatrix},
+\hat{\boldsymbol{\beta}}^{\mathrm{Ridge}}=\begin{bmatrix}\frac{11}{5+\lambda} \\ \frac{2}{1+\lambda}\end{bmatrix},
 $$
 <p>&nbsp;<br>
 

doc/pub/week36/html/week36-solarized.html

@@ -688,12 +688,12 @@ We will amongst other things show that the regularization parameter can reduce c
 Let us assume we have a data set with outputs/targets given by the vector
 
 $$
-\boldsymbol{y}=\begin{matrix}4 \\ 2 \\3\end{bmatrix},
+\boldsymbol{y}=\begin{bmatrix}4 \\ 2 \\3\end{bmatrix},
 $$
 
 and our inputs as a \( 3\times 2 \) design matrix
 $$
-\boldsymbol{X}=\begin{matrix}2 & 0\\ 0 & 1 \\ 1 & 0\end{bmatrix},
+\boldsymbol{X}=\begin{bmatrix}2 & 0\\ 0 & 1 \\ 1 & 0\end{bmatrix},
 $$
 
 meaning that we have two features and two unknown parameters \( \beta_0 \) and \( \beta_1 \) to be determined either by ordinary least squares, Ridge or Lasso regression.
@@ -713,7 +713,7 @@ $$
 Inserting the above values we obtain that 
 
 $$
-\hat{\boldsymbol{\beta}}^{\mathrm{OLS}}=\begin{matrix}\frac{11}{5} \\ 2\end{bmatrix},
+\hat{\boldsymbol{\beta}}^{\mathrm{OLS}}=\begin{bmatrix}\frac{11}{5} \\ 2\end{bmatrix},
 $$
 
 <p>
@@ -737,7 +737,7 @@ $$
 Inserting the above values we obtain that 
 
 $$
-\hat{\boldsymbol{\beta}}^{\mathrm{Ridge}}=\begin{matrix}\frac{11}{5+\lambda} \\ \frac{2}{1+\lambda}\end{bmatrix},
+\hat{\boldsymbol{\beta}}^{\mathrm{Ridge}}=\begin{bmatrix}\frac{11}{5+\lambda} \\ \frac{2}{1+\lambda}\end{bmatrix},
 $$
 
 <p>

doc/pub/week36/html/week36.html

@@ -693,12 +693,12 @@ We will amongst other things show that the regularization parameter can reduce c
 Let us assume we have a data set with outputs/targets given by the vector
 
 $$
-\boldsymbol{y}=\begin{matrix}4 \\ 2 \\3\end{bmatrix},
+\boldsymbol{y}=\begin{bmatrix}4 \\ 2 \\3\end{bmatrix},
 $$
 
 and our inputs as a \( 3\times 2 \) design matrix
 $$
-\boldsymbol{X}=\begin{matrix}2 & 0\\ 0 & 1 \\ 1 & 0\end{bmatrix},
+\boldsymbol{X}=\begin{bmatrix}2 & 0\\ 0 & 1 \\ 1 & 0\end{bmatrix},
 $$
 
 meaning that we have two features and two unknown parameters \( \beta_0 \) and \( \beta_1 \) to be determined either by ordinary least squares, Ridge or Lasso regression.
@@ -718,7 +718,7 @@ $$
 Inserting the above values we obtain that 
 
 $$
-\hat{\boldsymbol{\beta}}^{\mathrm{OLS}}=\begin{matrix}\frac{11}{5} \\ 2\end{bmatrix},
+\hat{\boldsymbol{\beta}}^{\mathrm{OLS}}=\begin{bmatrix}\frac{11}{5} \\ 2\end{bmatrix},
 $$
 
 <p>
@@ -742,7 +742,7 @@ $$
 Inserting the above values we obtain that 
 
 $$
-\hat{\boldsymbol{\beta}}^{\mathrm{Ridge}}=\begin{matrix}\frac{11}{5+\lambda} \\ \frac{2}{1+\lambda}\end{bmatrix},
+\hat{\boldsymbol{\beta}}^{\mathrm{Ridge}}=\begin{bmatrix}\frac{11}{5+\lambda} \\ \frac{2}{1+\lambda}\end{bmatrix},
 $$
 
 <p>

doc/pub/week36/ipynb/week36.ipynb

@@ -788,7 +788,7 @@
    "metadata": {},
    "source": [
     "$$\n",
-    "\\boldsymbol{y}=\\begin{matrix}4 \\\\ 2 \\\\3\\end{bmatrix},\n",
+    "\\boldsymbol{y}=\\begin{bmatrix}4 \\\\ 2 \\\\3\\end{bmatrix},\n",
     "$$"
    ]
   },
@@ -804,7 +804,7 @@
    "metadata": {},
    "source": [
     "$$\n",
-    "\\boldsymbol{X}=\\begin{matrix}2 & 0\\\\ 0 & 1 \\\\ 1 & 0\\end{bmatrix},\n",
+    "\\boldsymbol{X}=\\begin{bmatrix}2 & 0\\\\ 0 & 1 \\\\ 1 & 0\\end{bmatrix},\n",
     "$$"
    ]
   },
@@ -840,7 +840,7 @@
    "metadata": {},
    "source": [
     "$$\n",
-    "\\hat{\\boldsymbol{\\beta}}^{\\mathrm{OLS}}=\\begin{matrix}\\frac{11}{5} \\\\ 2\\end{bmatrix},\n",
+    "\\hat{\\boldsymbol{\\beta}}^{\\mathrm{OLS}}=\\begin{bmatrix}\\frac{11}{5} \\\\ 2\\end{bmatrix},\n",
     "$$"
    ]
   },
@@ -878,7 +878,7 @@
    "metadata": {},
    "source": [
     "$$\n",
-    "\\hat{\\boldsymbol{\\beta}}^{\\mathrm{Ridge}}=\\begin{matrix}\\frac{11}{5+\\lambda} \\\\ \\frac{2}{1+\\lambda}\\end{bmatrix},\n",
+    "\\hat{\\boldsymbol{\\beta}}^{\\mathrm{Ridge}}=\\begin{bmatrix}\\frac{11}{5+\\lambda} \\\\ \\frac{2}{1+\\lambda}\\end{bmatrix},\n",
     "$$"
    ]
   },

doc/src/week36/week36.do.txt

@@ -436,13 +436,13 @@ Let us assume we have a data set with outputs/targets given by the vector
 
 !bt
 \[
-\bm{y}=\begin{matrix}4 \\ 2 \\3\end{bmatrix},
+\bm{y}=\begin{bmatrix}4 \\ 2 \\3\end{bmatrix},
 \]
 !et
 and our inputs as a $3\times 2$ design matrix
 !bt
 \[
-\bm{X}=\begin{matrix}2 & 0\\ 0 & 1 \\ 1 & 0\end{bmatrix},
+\bm{X}=\begin{bmatrix}2 & 0\\ 0 & 1 \\ 1 & 0\end{bmatrix},
 \]
 !et
 meaning that we have two features and two unknown parameters $\beta_0$ and $\beta_1$ to be determined either by ordinary least squares, Ridge or Lasso regression.
@@ -461,7 +461,7 @@ Inserting the above values we obtain that
 
 !bt
 \[
-\hat{\bm{\beta}}^{\mathrm{OLS}}=\begin{matrix}\frac{11}{5} \\ 2\end{bmatrix},
+\hat{\bm{\beta}}^{\mathrm{OLS}}=\begin{bmatrix}\frac{11}{5} \\ 2\end{bmatrix},
 \]
 !et
 
@@ -483,7 +483,7 @@ Inserting the above values we obtain that
 
 !bt
 \[
-\hat{\bm{\beta}}^{\mathrm{Ridge}}=\begin{matrix}\frac{11}{5+\lambda} \\ \frac{2}{1+\lambda}\end{bmatrix},
+\hat{\bm{\beta}}^{\mathrm{Ridge}}=\begin{bmatrix}\frac{11}{5+\lambda} \\ \frac{2}{1+\lambda}\end{bmatrix},
 \]
 !et