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diff --git a/_images/8c3853104a59c370f5cc84d562ad51e9eabdacb7d5aa8a9fa016e4a25ec4fb28.png b/_images/8c3853104a59c370f5cc84d562ad51e9eabdacb7d5aa8a9fa016e4a25ec4fb28.png
diff --git a/_images/b78f6a52eae9f026621853aeab952a9cc96720f7ea9051c536f0ecf649833cd1.png b/_images/b78f6a52eae9f026621853aeab952a9cc96720f7ea9051c536f0ecf649833cd1.png
diff --git a/_sources/lectures/102-Varada-lectures/class_demos/demo_20_time-series.ipynb b/_sources/lectures/102-Varada-lectures/class_demos/demo_20_time-series.ipynb
diff --git a/lectures/101-Giulia-lectures/07_linear-models.html b/lectures/101-Giulia-lectures/07_linear-models.html
@@ -1744,8 +1744,8 @@ <h4>Predicting with learned weights<a class="headerlink" href="#predicting-with-
 <p>In our case, for values for the coefficient of <em>boring</em> &lt; -3.36, the prediction would be negative.</p>
 <p>A linear model learns these coefficients or weights from the training data!</p>
 <p>So a linear classifier is a linear function of the input <code class="docutils literal notranslate"><span class="pre">X</span></code>, followed by a threshold.</p>
-<div class="amsmath math notranslate nohighlight" id="equation-1c26b50b-107d-4735-803e-a8df9f8edf4b">
-<span class="eqno">(2)<a class="headerlink" href="#equation-1c26b50b-107d-4735-803e-a8df9f8edf4b" title="Permalink to this equation">#</a></span>\[\begin{equation}
+<div class="amsmath math notranslate nohighlight" id="equation-e752047d-c0ce-4a59-a92d-b427ac7bcf64">
+<span class="eqno">(2)<a class="headerlink" href="#equation-e752047d-c0ce-4a59-a92d-b427ac7bcf64" title="Permalink to this equation">#</a></span>\[\begin{equation}
 \begin{split}
 z =&amp; w_1x_1 + \dots + w_dx_d + b\\
 =&amp; w^Tx + b

diff --git a/lectures/102-Varada-lectures/class_demos/demo_20_time-series.html b/lectures/102-Varada-lectures/class_demos/demo_20_time-series.html
diff --git a/lectures/notes/07_linear-models.html b/lectures/notes/07_linear-models.html
@@ -1688,8 +1688,8 @@ <h4>Predicting with learned weights<a class="headerlink" href="#predicting-with-
 <p>In our case, for values for the coefficient of <em>boring</em> &lt; -3.36, the prediction would be negative.</p>
 <p>A linear model learns these coefficients or weights from the training data!</p>
 <p>So a linear classifier is a linear function of the input <code class="docutils literal notranslate"><span class="pre">X</span></code>, followed by a threshold.</p>
-<div class="amsmath math notranslate nohighlight" id="equation-4d44b704-b268-410c-abfd-ad9bfe556870">
-<span class="eqno">(1)<a class="headerlink" href="#equation-4d44b704-b268-410c-abfd-ad9bfe556870" title="Permalink to this equation">#</a></span>\[\begin{equation}
+<div class="amsmath math notranslate nohighlight" id="equation-3140ec92-ce24-4c9a-8d90-15c8542f69d6">
+<span class="eqno">(1)<a class="headerlink" href="#equation-3140ec92-ce24-4c9a-8d90-15c8542f69d6" title="Permalink to this equation">#</a></span>\[\begin{equation}
 \begin{split}
 z =&amp; w_1x_1 + \dots + w_dx_d + b\\
 =&amp; w^Tx + b

diff --git a/objects.inv b/objects.inv
diff --git a/searchindex.js b/searchindex.js