correcting typos
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@@ -436,13 +436,13 @@ Let us assume we have a data set with outputs/targets given by the vector
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!bt
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\[
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\bm{y}=\begin{matrix}4 \\ 2 \\3\end{bmatrix},
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\bm{y}=\begin{bmatrix}4 \\ 2 \\3\end{bmatrix},
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\]
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!et
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and our inputs as a $3\times 2$ design matrix
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!bt
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\[
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\bm{X}=\begin{matrix}2 & 0\\ 0 & 1 \\ 1 & 0\end{bmatrix},
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\bm{X}=\begin{bmatrix}2 & 0\\ 0 & 1 \\ 1 & 0\end{bmatrix},
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\]
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!et
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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.
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@@ -461,7 +461,7 @@ Inserting the above values we obtain that
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!bt
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\[
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\hat{\bm{\beta}}^{\mathrm{OLS}}=\begin{matrix}\frac{11}{5} \\ 2\end{bmatrix},
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\hat{\bm{\beta}}^{\mathrm{OLS}}=\begin{bmatrix}\frac{11}{5} \\ 2\end{bmatrix},
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\]
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!et
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@@ -483,7 +483,7 @@ Inserting the above values we obtain that
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!bt
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\[
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\hat{\bm{\beta}}^{\mathrm{Ridge}}=\begin{matrix}\frac{11}{5+\lambda} \\ \frac{2}{1+\lambda}\end{bmatrix},
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\hat{\bm{\beta}}^{\mathrm{Ridge}}=\begin{bmatrix}\frac{11}{5+\lambda} \\ \frac{2}{1+\lambda}\end{bmatrix},
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\]
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!et
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