updating lecture notes

doc/LectureNotes/_build/html/week36.html

@@ -1204,7 +1204,7 @@ eigenvalues ordered in a descending way, that is <span class="math notranslate n
 <p>In this case the standard OLS results in</p>
 <div class="math notranslate nohighlight">
 \[
-\boldsymbol{\theta}^{\mathrm{OLS}} = \boldsymbol{X}^T\boldsymbol{y}=\sum_{i=0}^{n-1}\boldsymbol{u}_i\boldsymbol{u}_i^T\boldsymbol{y},
+\boldsymbol{\theta}^{\mathrm{OLS}} = \boldsymbol{X}^T\boldsymbol{y},
 \]</div>
 <p>and</p>
 <div class="math notranslate nohighlight">
@@ -1214,7 +1214,7 @@ eigenvalues ordered in a descending way, that is <span class="math notranslate n
 <p>that is the Ridge estimator scales the OLS estimator by the inverse of a factor <span class="math notranslate nohighlight">\(1+\lambda\)</span>, and
 the Ridge estimator converges to zero when the hyperparameter goes to
 infinity.</p>
-<p>We will come back to more interpreations after we have gone through some of the statistical analysis part.</p>
+<p>We will come back to more interpretions after we have gone through some of the statistical analysis part.</p>
 <p>For more discussions of Ridge and Lasso regression, <a class="reference external" href="https://arxiv.org/abs/1509.09169">Wessel van Wieringen’s</a> article is highly recommended.
 Similarly, <a class="reference external" href="https://arxiv.org/abs/1803.08823">Mehta et al’s article</a> is also recommended.</p>
 </section>