typo

doc/LectureNotes/_build/html/project1.html

@@ -421,7 +421,7 @@ reports written like a standard scientific/technical report.  The
 links at
 <a class="github reference external" href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects">CompPhysics/MachineLearning</a>
 contain more information. There you can find examples of previous
-reports, the projects themselves, how we rade reports etc. How to
+reports, the projects themselves, how we grade reports etc. How to
 write reports will also be discussed during the various lab
 sessions. Please do ask us if you are in doubt.</p>
 <p>When using codes and material from other sources, you should refer to
@@ -651,7 +651,7 @@ include both Ridge and Lasso regression in the final analysis.</p>
 <h2>Background literature<a class="headerlink" href="#background-literature" title="Link to this heading">#</a></h2>
 <ol class="arabic simple">
 <li><p>For a discussion and derivation of the variances and mean squared errors using linear regression, see the <a class="reference external" href="https://arxiv.org/abs/1509.09169">Lecture notes on ridge regression by Wessel N. van Wieringen</a></p></li>
-<li><p>The textbook of <a class="reference external" href="https://www.springer.com/gp/book/9780387848570">Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer</a>, chapters 3 and 7 are the most relevant ones for the analysis here.</p></li>
+<li><p>The textbook of <a class="reference external" href="https://www.springer.com/gp/book/9780387848570">Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer</a>, chapters 3 and 7 are the most relevant ones for the analysis of parts g) and h).</p></li>
 </ol>
 </section>
 <section id="introduction-to-numerical-projects">