update

doc/LectureNotes/_build/html/week38.html

@@ -397,8 +397,8 @@ const thebe_selector_output = ".output, .cell_output"
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#material-for-lecture-thursday-september-21">
-   Material for lecture Thursday September 21
+  <a class="reference internal nav-link" href="#material-for-lecture-monday-september-16">
+   Material for lecture Monday September 16
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
@@ -491,11 +491,6 @@ const thebe_selector_output = ".output, .cell_output"
    More classes
   </a>
  </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#friday-september-23">
-   Friday September 23
-  </a>
- </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#searching-for-optimal-regularization-parameters-lambda">
    Searching for Optimal Regularization Parameters
@@ -747,8 +742,8 @@ const thebe_selector_output = ".output, .cell_output"
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#material-for-lecture-thursday-september-21">
-   Material for lecture Thursday September 21
+  <a class="reference internal nav-link" href="#material-for-lecture-monday-september-16">
+   Material for lecture Monday September 16
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
@@ -841,11 +836,6 @@ const thebe_selector_output = ".output, .cell_output"
    More classes
   </a>
  </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#friday-september-23">
-   Friday September 23
-  </a>
- </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#searching-for-optimal-regularization-parameters-lambda">
    Searching for Optimal Regularization Parameters
@@ -1111,8 +1101,8 @@ doconce format html week38.do.txt --no_mako -->
 <li><p>Work on project 1, in particular resampling methods like cross-validation and bootstrap.</p></li>
 </ul>
 </div>
-<div class="section" id="material-for-lecture-thursday-september-21">
-<h2>Material for lecture Thursday September 21<a class="headerlink" href="#material-for-lecture-thursday-september-21" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="material-for-lecture-monday-september-16">
+<h2>Material for lecture Monday September 16<a class="headerlink" href="#material-for-lecture-monday-september-16" title="Permalink to this headline">¶</a></h2>
 </div>
 <div class="section" id="logistic-regression">
 <h2>Logistic Regression<a class="headerlink" href="#logistic-regression" title="Permalink to this headline">¶</a></h2>
@@ -1599,9 +1589,6 @@ descent method.  Newton’s method and gradient descent methods are
 discussed in the material on <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/pub/Splines/html/Splines-bs.html">optimization
 methods</a>.</p>
 </div>
-<div class="section" id="friday-september-23">
-<h2>Friday September 23<a class="headerlink" href="#friday-september-23" title="Permalink to this headline">¶</a></h2>
-</div>
 <div class="section" id="searching-for-optimal-regularization-parameters-lambda">
 <h2>Searching for Optimal Regularization Parameters <span class="math notranslate nohighlight">\(\lambda\)</span><a class="headerlink" href="#searching-for-optimal-regularization-parameters-lambda" title="Permalink to this headline">¶</a></h2>
 <p>In project 1, when using Ridge and Lasso regression, we end up

doc/LectureNotes/_build/jupyter_execute/week38.py

@@ -49,7 +49,7 @@
 # 
 #   * Work on project 1, in particular resampling methods like cross-validation and bootstrap.
 
-# ## Material for lecture Thursday September 21
+# ## Material for lecture Monday September 16
 
 # ## Logistic Regression
 # 
@@ -512,8 +512,6 @@ plt.show()
 # discussed in the material on [optimization
 # methods](https://compphysics.github.io/MachineLearning/doc/pub/Splines/html/Splines-bs.html).
 
-# ## Friday September 23
-
 # ## Searching for Optimal Regularization Parameters $\lambda$
 # 
 # In project 1, when using Ridge and Lasso regression, we end up