deploy: 8e92865

README.html

@@ -578,7 +578,7 @@ <h2>Deliverable due dates (tentative)<a class="headerlink" href="#deliverable-du
 </tr>
 <tr class="row-even"><td><p>hw8</p></td>
 <td><p>November 25, 11:59 pm</p></td>
-<td><p>[GitHub repo(https://github.com/new?template_name=hw8&amp;template_owner=ubc-cpsc330)</p></td>
+<td><p><a class="reference external" href="https://github.com/new?template_name=hw8&amp;amp;template_owner=ubc-cpsc330">GitHub repo</a></p></td>
 <td><p><a class="reference external" href="https://www.gradescope.ca/courses/18608">Gradescope</a></p></td>
 </tr>
 <tr class="row-odd"><td><p>hw9</p></td>

_sources/README.md

@@ -68,7 +68,7 @@ Usually the homework assignments will be due on Mondays (except next week) and w
 | hw6            | ~~November 04~~ Nov 6th, 11:59 pm | [GitHub repo](https://github.com/new?template_name=hw6&template_owner=ubc-cpsc330) | [Gradescope](https://www.gradescope.ca/courses/18608) |
 | **Midterm 2**  | Nov 14 and Nov 15     | PrairieLearn (CBTF, in person)                                                     | PrairieLearn (CBTF, in person)                        |
 | hw7            | November 18, 11:59 pm | [GitHub repo](https://github.com/new?template_name=hw7&template_owner=ubc-cpsc330) | [Gradescope](https://www.gradescope.ca/courses/18608) |
-| hw8            | November 25, 11:59 pm | [GitHub repo(https://github.com/new?template_name=hw8&template_owner=ubc-cpsc330) | [Gradescope](https://www.gradescope.ca/courses/18608) |
+| hw8            | November 25, 11:59 pm | [GitHub repo](https://github.com/new?template_name=hw8&template_owner=ubc-cpsc330)  | [Gradescope](https://www.gradescope.ca/courses/18608) |
 | hw9            | December 05, 11:59 pm | GitHub repo[](https://github.com/new?template_name=hw9&template_owner=ubc-cpsc330) | [Gradescope](https://www.gradescope.ca/courses/18608) |
 | **Final exam** | TBA                   | PrairieLearn (CBTF, in person)                                                           | PrairieLearn (CBTF, in person)                              |
 

lectures/101-Giulia-lectures/07_linear-models.html

@@ -1743,8 +1743,8 @@ <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-9f0672fe-afc6-4246-96f7-4a477d174e74">
-<span class="eqno">(2)<a class="headerlink" href="#equation-9f0672fe-afc6-4246-96f7-4a477d174e74" title="Permalink to this equation">#</a></span>\[\begin{equation}
+<div class="amsmath math notranslate nohighlight" id="equation-dbc068dd-0c2c-489f-8aa1-3d7f0ff871ec">
+<span class="eqno">(2)<a class="headerlink" href="#equation-dbc068dd-0c2c-489f-8aa1-3d7f0ff871ec" 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

lectures/notes/07_linear-models.html

@@ -1687,8 +1687,8 @@ <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-a104d6db-c6a8-4f98-9b0f-bbdea9b96eb3">
-<span class="eqno">(1)<a class="headerlink" href="#equation-a104d6db-c6a8-4f98-9b0f-bbdea9b96eb3" title="Permalink to this equation">#</a></span>\[\begin{equation}
+<div class="amsmath math notranslate nohighlight" id="equation-fba4fe39-3ab9-451d-925b-eddc53f5520d">
+<span class="eqno">(1)<a class="headerlink" href="#equation-fba4fe39-3ab9-451d-925b-eddc53f5520d" 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