typo

Author: mhjensen

doc/pub/week39/html/week39-reveal.html

@@ -433,9 +433,6 @@ <h2 id="the-bias-variance-tradeoff">The bias-variance tradeoff </h2>
 $$
 <p>&nbsp;<br>
 
-
-<b>Note that in order to derive these equations we have assumed we can replace the unknown function \( {\bm f} \) with the target/output data \( \boldsymbol{y} \).</b>
-
 <p>The three terms represent the square of the bias of the learning
 method, which can be thought of as the error caused by the simplifying
 assumptions built into the method. The second term represents the
@@ -467,6 +464,8 @@ <h2 id="the-bias-variance-tradeoff">The bias-variance tradeoff </h2>
 <p>&nbsp;<br>
 
 <p>that is the rewriting in terms of the so-called bias, the variance of the model \( \boldsymbol{\tilde{y}} \) and the variance of \( \boldsymbol{\epsilon} \).</p>
+
+<b>Note that in order to derive these equations we have assumed we can replace the unknown function \( \boldsymbol{f} \) with the target/output data \( \boldsymbol{y} \).</b>
 </section>
 
 <section>

doc/pub/week39/html/week39-solarized.html

@@ -454,9 +454,6 @@ <h2 id="the-bias-variance-tradeoff">The bias-variance tradeoff </h2>
 \mathbb{E}\left[(\boldsymbol{y}-\boldsymbol{\tilde{y}})^2\right]=\frac{1}{n}\sum_i(f_i-\mathbb{E}\left[\boldsymbol{\tilde{y}}\right])^2+\frac{1}{n}\sum_i(\tilde{y}_i-\mathbb{E}\left[\boldsymbol{\tilde{y}}\right])^2+\sigma^2.
 $$
 
-
-<b>Note that in order to derive these equations we have assumed we can replace the unknown function \( {\bm f} \) with the target/output data \( \boldsymbol{y} \).</b>
-
 <p>The three terms represent the square of the bias of the learning
 method, which can be thought of as the error caused by the simplifying
 assumptions built into the method. The second term represents the
@@ -483,6 +480,8 @@ <h2 id="the-bias-variance-tradeoff">The bias-variance tradeoff </h2>
 
 <p>that is the rewriting in terms of the so-called bias, the variance of the model \( \boldsymbol{\tilde{y}} \) and the variance of \( \boldsymbol{\epsilon} \).</p>
 
+<b>Note that in order to derive these equations we have assumed we can replace the unknown function \( \boldsymbol{f} \) with the target/output data \( \boldsymbol{y} \).</b>
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="a-way-to-read-the-bias-variance-tradeoff">A way to Read the Bias-Variance Tradeoff </h2>
 

doc/pub/week39/html/week39.html

@@ -531,9 +531,6 @@ <h2 id="the-bias-variance-tradeoff">The bias-variance tradeoff </h2>
 \mathbb{E}\left[(\boldsymbol{y}-\boldsymbol{\tilde{y}})^2\right]=\frac{1}{n}\sum_i(f_i-\mathbb{E}\left[\boldsymbol{\tilde{y}}\right])^2+\frac{1}{n}\sum_i(\tilde{y}_i-\mathbb{E}\left[\boldsymbol{\tilde{y}}\right])^2+\sigma^2.
 $$
 
-
-<b>Note that in order to derive these equations we have assumed we can replace the unknown function \( {\bm f} \) with the target/output data \( \boldsymbol{y} \).</b>
-
 <p>The three terms represent the square of the bias of the learning
 method, which can be thought of as the error caused by the simplifying
 assumptions built into the method. The second term represents the
@@ -560,6 +557,8 @@ <h2 id="the-bias-variance-tradeoff">The bias-variance tradeoff </h2>
 
 <p>that is the rewriting in terms of the so-called bias, the variance of the model \( \boldsymbol{\tilde{y}} \) and the variance of \( \boldsymbol{\epsilon} \).</p>
 
+<b>Note that in order to derive these equations we have assumed we can replace the unknown function \( \boldsymbol{f} \) with the target/output data \( \boldsymbol{y} \).</b>
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="a-way-to-read-the-bias-variance-tradeoff">A way to Read the Bias-Variance Tradeoff </h2>